Mobile Ad Hoc Networks: Current Status and Future Trends

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Kahn, Robert E. January COM 1 : — From Wikipedia, the free encyclopedia. It has been suggested that this article be merged into Wireless ad hoc network. Discuss Proposed since January Kluwer Academic Press. Zanjireh; Hadi Larijani May Glasgow, Scotland. Prentice Hall PTR. Turing Award. Association for Computing Machinery.

Burchfiel; R. Tomlinson ; M. Beeler May Functions and structure of a packet radio station PDF. National Computer Conference and Exhibition. Retrieved WiFi networks on drones. Mobility increases the capacity of ad-hoc wireless networks. IEEE Proceedings. Handbook of wireless networks and mobile computing. Bibcode : Natur. Physics Letters A. Bibcode : PhLA.. Wireless Communications Journal.

Bibcode : ISenJ.. Channel access methods and media access control. Categories : Channel access methods Radio resource management Wireless networking. Hidden categories: Articles to be merged from January All articles to be merged CS1: long volume value. Namespaces Article Talk. This is because in the DCG method, the number of mobile nodes in a group is very large 40 in most cases and thus replica relocation rarely occurs.

From the simulation result, we can confirm that the DCG method gives the highest accessibility, while the SAF method produces the lowest traffic. The DAFN method shows the balanced performance i. Therefore, in a real environment, a proper method among the three methods should be chosen based on the system requirement, i.

For example, when either network bandwidth or node battery is very limited, the SAF method should be the best. After our first paper on replica placement, 35 we extended the methods proposed there, in several respects, including remaining batteries life and mutual dependency among data items. In Ref. For example, at a disaster site, two data items, relating respectively to structural damage information and the progress of rescuer activity at the same location, are often accessed simultaneously.

Thus, in the extended methods, we replaced the data access frequency to each data item, with the access frequency to pairs of data items accessed simultaneously. By doing so, the proposed methods can prevent mobile nodes from exhausting their batteries, which is very important in MANET environments, because such battery exhaustion has a negative impact, both in terms of data availability i. The above studies basically assumed a simple environment where data updates do not occur. We extended the methods proposed in Ref. The common idea among these studies 37 , 50 , 51 is that the remaining time until each data item is updated next is taken into account when deciding data items replicated.

More specifically, this remaining time is defined as the profit to replicate the data item as well as the data access frequency. Finally, in Ref. In Section 2 except for Section 2. However, in many real-world applications, data updates occur; and in MANETs, due mainly to nodal movement, disappearance disconnection of nodes and network partitioning frequently occur, and thus it is quite difficult to consistently and completely update all the versions of the replicas. And if non-updated versions of replicas exist, data access read operations will read old replicas, which is invalid in most applications.

Reading old replicas is wasteful in terms of system features e. Moreover, in the case of many real-world applications, reading old data items is simply unacceptable, as it may have a seriously negative impact on real-world activities; for example, rescue operations based on out-of-date information may be significantly impaired. There are basically two types of approach to update management in traditional database systems, which are also applicable in MANETs: optimistic and pessimistic. In an optimistic approach, when a mobile node issues an access read request for a data item, but is not connected to the node holding the original copy, it tentatively accesses one of the replicas held by mobile nodes to which it is connected.

The validity of the tentative access i. As this form of update management often results in read operations that access stale old replicas, which may require rollbacks of the performed operations, it is typically difficult, if not impossible, to apply it in a MANET, where update write operations can be issued by any node.

Therefore, in our research, we assume that optimistic approaches are used only in MANETs where the node holding the original copy issues write operations. In such an environment, there are basically two technical challenges: invalidation of old replicas, and dissemination of the latest up-to-date data items.

In a pessimistic approach, all access read operations must be confirmed to be consistent valid on the spot, during the operation period. However, it is very difficult to achieve this form of update management in MANETs, because of the frequency of nodal disconnections; and thus, further advances are needed in this area.

In this section, we summarize our studies on update management based on the two approaches above. Here we assume that all data items are updated at inconstant intervals; and after a given data item is updated, all its replicas basically become invalid, meaning that read operations are valid if and only if they are performed on up-to-date i.

Since, in optimistic approaches, the tentative read operations on replicas may be invalid, there are several performance metrics. Read operations success rate Success rate : As in the problem of replica placement, the read operation success rate is of course the most significant performance metric. Rate of dirty read operations Dirty-read rate : Since read operations on invalid old replicas i. Even if the read operation success rate is high, some applications cannot accept too high dirty-read rate.

Furthermore, increasing the success rate is generally accompanied by an increase in the dirty-read rate i. Delay until the validation of tentative read operations Delay : Tentative read operations on replicas are only later validated, and thus the read-operation issuer incurs some delay until the validation. For most applications, a shorter delay is desirable. Extra traffic Traffic : To improve the results of the performance metrics above, a number of useful techniques have been developed, including invalidation of old replicas, and dissemination of the latest up-to-date data items.

However, such approaches incur extra overhead in terms of communication traffic; and large volumes of traffic make significant consumption demands on network bandwidth and energy, which are not desirable in MANETs. In the following, we summarize our studies on optimistic approaches to update management, which aim to improve the values in the above metrics.

Data management issues in mobile ad hoc networks

Again, in optimistic approaches, we assume that only the node holding the original copy issues write operations i. We also present the result of a performance study to show the effectiveness of our approaches. Due to the limitation of space, we only show it for updated-data dissemination. In order to reduce the dirty-read rate and delay, with low traffic overhead, invalidating old replicas is often used in traditional distributed systems and mobile systems.

Here, we assume that each mobile node manages a table in which information on the time stamps of all the data items in the entire MANET is recorded. A time stamp is the latest update time of the corresponding data item, known by the mobile node, which may differ from the actual latest update time. This table is called the time stamp table. By broadcasting an invalidation report more frequently, we can reduce the dirty-read rate and delay more effectively, but the traffic increases. The two methods, UB and CR, adopt different strategies on how to optimize the combination of dirty-read rate, delay, and traffic.

We outline the two methods below. It should be noted that the old-replica invalidation methods have no impact on the first metric i. In the UB method, a mobile node holding an original copy broadcasts an invalidation report to connected mobile nodes each time it updates the data item. The invalidation report includes the following information:.

If a mobile node that receives the invalidation report holds a replica of the corresponding data item, it confirms whether the replica it holds is valid; discarding it if it is stale. It also updates its own time stamp table.

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In this method, the traffic caused by sending invalidation reports is small, because mobile nodes broadcast only when they update the original copy. Mobile nodes connected to the owner of the original copy can maintain the most up-to-date time stamp of the corresponding data item. However, since the network topology frequently changes due to nodal movement, connected mobile nodes may have different time stamps and different versions of the replicas for the same data item. In the CR method, similarly to the UB method, a mobile node holding an original copy broadcasts an invalidation report to connected mobile nodes each time it updates the data item.

But in addition, whenever two mobile nodes are newly connected with each other, they re-broadcast the invalidation reports they have previously received. More specifically, the two newly connected mobile nodes share their respective time stamp tables, and each compares the respective entries for each data item. If the time stamp for a data item in the received time stamp table is more recent i. Then, each of the two nodes re-broadcasts, to the previously connected mobile nodes before the current new connection , invalidation reports for those data items whose received time stamps were greater i.

Mobile nodes that receive these invalidation reports discard their replicas in the same manner as in the UB method. Thus, in the CR method, connected mobile nodes can maintain the same time stamp tables, because invalidation reports are re-broadcast whenever two mobile nodes are newly connected. Moreover, invalidation reports are disseminated among a larger number of mobile nodes than in the UB method, and old replicas are effectively discarded even if the replica owners are not connected to the mobile nodes holding the original copies.

Thus, this method can further reduce the number of dirty-read operations, in comparison with the UB method. However, when the network topology frequently changes, the traffic caused by sending invalidation reports is much higher in the CR method, due to the frequent broadcasts of the reports. When a node issuing a read operation access request connects with the node holding the original copy of the target data item, it performs the operation on the original copy.

If, on the other hand, it connects with a replica holder, it performs a tentative operation on the replica with the latest time stamp; and when it later connects with the mobile node holding the original copy, the success or failure of the tentative data read is confirmed. As aforementioned, the old-replica invalidation methods presented above can reduce the dirty-read rate and delay, with low traffic overhead, but cannot increase the success rate, because they simply invalidate old replicas.

Thus, in Refs. Updated data items are disseminated after completing the old-replica invalidation procedures proposed in Ref. The basic idea is very simple, with updated data items as well as invalidation reports being disseminated. To this end, our proposed methods follow similar strategies to the old-replica invalidation methods described in Ref. Let us summarize the methods proposed in Refs. In the DU method, old-replica invalidation is first performed, as in the UB method.

But here, each mobile node that discards one of its replicas refreshes it by requesting the updated up-to-date data item from the mobile node holding the original copy. This produces just low traffic, because the dissemination of an updated data item is performed only when the data item is updated. However, since only mobile nodes connected to the original copy holder can receive the latest version, the success rate is not appreciably improved.

In the DC method, the procedure followed when the original copy holder updates its own original copy is the same as in the DU method. But in addition, whenever two mobile nodes newly connect with each other, replica invalidation is performed as in the CR method, and these two nodes then disseminate the updated data items. And here, as aforementioned, we proposed two variations, which differ in terms of the range over which updated data items are disseminated. We briefly present the result of a simulation study. These nodes initially locate at random positions and move according to the random waypoint model in which each node randomly determines a destination position and moves toward it with a randomly determined speed, and repeats this behavior.

The data accessibility i. The rate of accessing invalid replicas i. The traffic is defined as the total data volume for transmitting updated data items and control packets. As the average update period increases, in all of the proposed methods, the data accessibility improves because the replicas held by each mobile node are valid for a longer time.

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In summary, the simulation result shows that the DC method reduces the rate of accessing invalid replicas, but produces higher traffic than the DU method. In real environments, the most appropriate method should be chosen among the proposed methods according to the update frequencies of data items and system requirements. As aforementioned, some applications require that the validity of data operations read and write must be confirmed immediately i. Therefore, in a pessimistic approach, the validity consistency of each performed data operation is confirmed on the spot, during the operation period.

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Meanwhile, strict global consistency of data operations on replicas is not desirable in many applications, as it is too costly and difficult to achieve. Thus, new consistency maintenance, based on local conditions such as location and time, must be investigated. Here, we briefly describe these consistency levels and protocols.

First, we outline the system model assumed, and then describe the proposed approaches. This assumption is based on the fact that it is usually difficult to centrally maintain consistency over the entire network. We also assumed that the MANET consist of two kinds of mobile nodes: proxies and peers , where proxies are specially designated peers who manage other peers in a specific MANET region, and every node including proxies knows all the proxies in the network.

A good example is a situation in which the members of a rescue service are divided into several groups, each of which is responsible for a certain region, and information on the progress of the tasks assigned to each group is shared over the entire network.

To achieve GC, we adopt dynamic quorums similar to Ref. The consistency is hierarchically managed at two levels: among the peers in a given region, and among proxies. The node which issues an operation write or read first sends a request message to the proxy of its region, which we call the coordinator. Then, the coordinator attempts to set the necessary number of local locks, QLW i QLR i , for replicas held by peers in its region of responsibility.

If it succeeds, the global lock is set in the region. At the same time, the coordinator successively forwards the request message to other proxies, until it successfully sets the requisite number of global locks, QW QR , with each proxy that receives the request attempting to set the necessary number of local locks i. Finally, if the coordinator succeeds in setting the necessary numbers, QW QR and QLW i QLR i , of global and local locks, among proxies and peers, the write read operation is performed on the replicas for which the global and local read write locks have been set.

In read operations, the operation is performed on the most recent version of a given replica, among those with locks. In write operations, the operation is performed on all the replicas with locks. Therefore, in this example, the write operation is successfully performed. Data operation consistency is required only in each region of interest, and this consistency level weakens the strictness of consistency from a spatial perspective. An example of an application environment requiring LC is a situation in which the members of a rescue service share damage information, such as the number of injured persons and damaged buildings, which consists of distinct data items reflecting the varying extent of the damage.

This information is used locally by the leaders in each region, to decide on resource allocation and task schedules in their respective groups. To achieve LC, consistency is maintained only among peers in each region, in a manner similar to GC. In TC, replicas are valid even if their versions are different but a predetermined time validity period T has not yet passed since they were last updated.

This consistency level weakens the strictness of consistency from a temporal perspective. In TC, typically, read and write operations are performed locally on the operation issuing peers. A read operation is performed if the operation issuing peer holds a valid replica. Otherwise, the node searches for a mobile node that holds a valid replica. Here, data operation consistency is required only in each peer. Thus, this consistency level further weakens the strictness of consistency, in comparison with LC, and is the weakest from a spatial perspective.

In PC, read and write operations are performed locally on the operation issuing peers. These nodes initially locate at random positions in their assigned region 20 nodes are assigned to each region , and move according to the random waypoint model within the region. The performance metrics are success ratio Fig. We measured these metrics for both read and write operations. The success ratio is the same as data accessibility in section 2. Performance comparison unlimited memory and limited movement. This is because the connectivity among mobile nodes becomes lower.

We can see an interesting fact that when the area size is larger than , the success ratio in GC suddenly gets lower but in LC remains high. This fact shows that even when the connectivity among mobile nodes is still high in each region, the connectivity among proxies becomes low, i.

The success ratio of write operations in TC and those of write and read operations in PC are always 1 because every peer can perform operations locally. The success ratio of read operations in TC gets lower as the area size gets larger. This is because when the connectivity is low, mobile nodes cannot access valid replicas held by connected mobile nodes with high probability. This is because the traffic firstly increases due to the increase of hop-count for communication, and then decreases due to the decrease of the number of neighbors i.

Obviously, the message traffic of write operations in TC and those of write and read operations in PC are always 0. If the sizes of messages and data items are given, it is determined which one is dominant. The data traffic of GC is much higher than LC for both write and read operations. The data traffic for read operations in TC is much higher than other protocols, because there are fewer valid replicas in TC, and request-issuing peers have to obtain valid replicas from far away peers.


In summary, we can confirm from the simulation result that all the four consistency management methods have quite different performance. Of course, the consistency level should be chosen based on the system requirement. The simulation result also suggests us that we should not choose unnecessarily strong consistency level because higher consistency level may cause significant degradation of success ratio and increase of traffic. As noted in Section 1.

Here, focusing on complex types of queries, which specify certain query conditions that define the data items sought by the query issuing node, we describe our studies on top- k searches and k -nearest neighbor k NN searches, which are typical examples of such queries. Due to the limitation of space, we only show it for k NN query processing.

As it is important to efficiently acquire only necessary data items in MANETs, top- k queries offer a promising approach. In a top- k query, data items are ordered according to their score, calculated based on a specific set of attribute values using some scoring function, and the query-issuing node acquires the data items with the k highest scores. The basic means to achieve this consists in attaching a small but critical piece of information to each query message, which effectively narrows down the data item candidates to those included in the final top- k result.

To this end, each mobile node roughly identifies data items with the k highest scores, and designates some of these scores as Standard Scores SS ; then, as mobile nodes transmit query and reply messages, they reduce the number of candidates included in the top- k result, by referring to these SSs. Furthermore, if a mobile node detects the disconnection of a necessary radio link during the transmission of a reply message, it searches for an alternate path along which to transmit the reply message to the query-issuing node.

The procedures for the query-issuing node, M p , and for the mobile nodes receiving a query message, are briefly explained below. First, M p specifies the number of requested data items, k , and the query conditions. Then, M p calculates the scores of its data items based on the query conditions, using a scoring function, and initializes its SSs as follows:. Here, S i , h denotes the h -th highest score among the scores calculated by M i. Specifically, the SSs of M p are the every -th scores calculated by M p.

Then, M p transmits a query message, with the attached SSs, to its neighboring mobile nodes. Each mobile node that receives the query message updates the SSs based on the scores of its retained data items, and forwards the revised query message to its neighbors. The reply messages are sent back to M p along the same routes through which the query was disseminated.

Based on the information in the reply message, each mobile node that relays the reply sets its own threshold, ensuring that this is equal to or greater than the k -th highest score in the network, and sends to M p its retained data items with scores equal to or greater than the threshold Fig. In this way, mobile nodes can reduce the number of data item candidates included in the top- k result, which helps to reduce the traffic required for query processing.

After Ref. For example, in Ref. For estimation of score distribution, these methods utilize either a histogram of scores or an approximation to a regular distribution based on the scores of data items obtained during query processing. Therefore, the proposed methods also take replication into account in query processing. Specifically, these methods try to avoid duplicate transmissions of replicas of same data items, and also minimize the length of paths along which the data items are replied.

By doing so, we can reduce both the traffic and delay for query processing. To this end, our scheme not only takes into account scores of data items, but also adopts a randomized approach to achieve diversity of replicas in the entire MANET. Since MANETs are generally constructed of collaborating mobile users who are geographically distributed in the working area, location based queries such as that finding users near a specific location and that finding data associated with a specific location e. A naive approach for searching k NN nodes is that flooding the entire MANET with a query message and receiving a reply from each query receiving node, which includes the location of the node.

Obviously, this produces too much unnecessary traffic, resulting in not only consuming a large amount of energy but also reducing the accuracy of the query result due to packet losses caused by the heavy traffic. Therefore, in Ref. These methods are based on the following key policy.

To this end, in the proposed methods, the query-issuing node first forwards a k NN query using geo-routing to the nearest node from the location specified by the query query point. Then, the nearest node from the query point forwards the query to other nodes close to the query point, and each node receiving the query replies with the information on itself.

A possible way to achieve this is that every node continuously recognizes the locations of neighbor nodes by exchanging beacons in other words, hello or heart-beat messages. Thus, our methods were designed as beacon-less approaches. How to achieve this is explained later in this subsection. In the EXP method, the nearest node from the query point broadcasts the query to nodes within a specific circular region, and each node receiving the query replies with information on itself Fig. If the node density around the query point is significantly different from that in the entire MANET, the EXP method cannot collect the k NN low estimation or collects the information on an unnecessarily large number of nodes high estimation.

In the SPI method, the nearest node from the query point forwards the query to other nodes in a spiral manner, and the node that collects a satisfactory k NN result transmits the result to the query-issuing node Fig. Thus, this method does not require to specify a circular region. To achieve this, the entire area is dynamically partitioned into a set of hexagonal cells whose size is determined based on the communication range of the mobile nodes so that the information on nodes in a cell can be obtained through on-hop communication , with the query point at the center of a hexagonal cell.

In both methods, a designated node aggregates the information in the received replies, and thus unnecessary information is not sent in reply through a long path to the query-issuing node. In these ways, unnecessary transmissions of queries and replies can be reduced. The main idea of making the above methods beacon-less i.

For example, in the EXP method, after broadcasting a query within the circular region, it is achieved that query replies start to be sent from farther nodes to closer nodes by setting the waiting time RD as follows. These nodes initially locate at random positions and move according to the random waypoint model. For a purpose of comparison, we also show the performances of the naive method and the DIKNN method as well as beacon-based versions of our methods. The naive method does not use beacons, but the query-issuing node first floods a query within the range, which is determined in the same way as the EXP method, and then each node receiving the query individually replies to the query-issuing node.

In the EXP and SPI methods using beacons, the node behavior is basically the same as in the beacon-less EXP and SPI methods, however messages are transmitted unicast based on the neighbor information obtained by beacons. Performance comparison nodes. The performance metrics are traffic, response time, and accuracy of query result. The traffic is defined as the average total volume of query messages and replies exchanged in processing a query. The response time is defined as the average time from the transmission of a query message by the query-issuing node, to the reception of the k NN result.

The accuracy of query result is defined as the average of the weighted ratio MAP value 73 of the number of k NNs whose information is included in the k NN result acquired by the query-issuing node, to the requested number of k NNs, k. Our proposed methods generate far less traffic than the methods using beacons, as the periodical beacon exchanges involved in the latter cause a great deal of traffic. Our proposed methods also generate far less traffic than the naive method, because in our methods, replies are sent back to the query-issuing node in a more efficient manner.

In the SPI method, the traffic depends on the number of laps required for collecting the information on k NNs, and thus the traffic increases in a stepwise manner as k increases. In our methods, a node sets a waiting time before transmitting a message which is a disadvantage of not using beacons , and thus the response time is increased. In the EXP method in particular, every node must wait for calculated waiting time before sending back a reply, which results in an increase in response time.

In the naive method, such waiting times do not occur; however, in this method retransmissions of replies often occur, due to packet losses caused by the increased traffic. Overall, the response time in the naive method is roughly similar to that of the SPI method. This is because, in the latter, packet losses often occur due to individual replies from a large number of nodes.

In summary, our proposed beacon-less k NN query processing methods which were specially designed for MANETs perform significantly better than the naive method and the state-of-the-art approach for WSNs. Of our proposed methods, the SPI method achieves high performance when the node density is high nodes.

However, although not presented in this review, the result in Ref. Thus, the EXP method also has an advantage that it can stably achieve both reduction in traffic and high accuracy of the query result. We extended the EXP method to retrieve location-dependent data items which are associated with some locations. Therefore, the method proposed in Ref. In our studies described in the above sections, we do not assume the presence of malicious nodes. If malicious nodes are present, the accuracy of query processing is expected to decrease.

In Refs. Here, in top- k query processing, a query-issuing node does not know the global top- k result beforehand. Therefore, even if a malicious node has performed a DRA, the query-issuing node considers all the received data items with the k highest scores to be the global top- k result, rendering the DRA effectively undetectable, i. In this section, we present our approaches against DRAs, and also present the result of a performance study to show the effectiveness of our approaches. The first action is needed to reduce the impact of DRAs from the application perspective.

The second action is needed to remove the malicious nodes and keep the MANET healthy from the system perspective. Moreover, in Ref. Here, for the purpose of simplicity, we assumed a naive manner for top- k query processing where the query-issuing node first broadcasts a query over the entire MANET and each nodes receiving the query sends back a reply with data items with the k highest scores local top-k result among its own data items and all data items received from its child nodes on the query propagation routes, i. The idea for keeping high accuracy of the query result is simple but effective: each node receiving a query replies with data items with the k highest scores along multiple two routes.

By doing so, even if a malicious node is present along a query reply path, the query-issuing node can acquire the top- k result. Our experimental results confirmed us that even if more than two malicious nodes are present, just replying along two routes works well in most cases. To enable DRA and malicious node detection, we make each reply message include information on the route along which the message is forwarded. By doing so, the query-issuing node can know which data items should be sent back along the route, in other words, it can recognize a DRA.

When detecting a DRA, the query-issuing node narrows down the malicious node candidates from the information attached in the received reply messages, and inquires with non-candidate nodes neighbors of the candidates information on the data items sent by these candidates, allowing it to identify the malicious nodes. Through simulation experiments, we confirmed that when there are multiple malicious nodes in a MANET, it is difficult for the method proposed in Ref. This is partly because nodes, in this method, are more likely to only identify malicious nodes near their own location than those farther away.

Thus, in order to rapidly identify a greater number of malicious nodes, a global malicious node identification method proposed in Ref. In this method, after receiving a predetermined number of queries, each node divides all nodes into some groups based on the similarity of the information on identified malicious nodes which was sent from them.

Then, the node performs malicious node identification separately with each group, based on the information in the group, and comprehensively makes the final judgment of malicious nodes based on the identification results of all the groups. In this method, even if malicious nodes claim some normal nodes as malicious which we call false notification attack FNA , there is a decisive difference in the nature of the information possessed by normal and malicious nodes concerning the identified malicious nodes, and therefore, the malicious nodes can be easily identified.

By the method in Ref. However, the global malicious node identification is performed only after each node receives a predetermined number of queries, which still needs relatively long time to identify all malicious nodes. In addition, it sometimes happens that some normal nodes are determined as malicious by mistake.

To solve these problems and identify all malicious nodes more quickly, we proposed a signature-based top- k query processing method in Ref. In this method, each node receiving a query message sends back a reply message which contains the local top- k result i. By doing so, the query-issuing node can know the data items sent by each node in the MANET, and thereby can identify malicious nodes using the received signatures.

After identifying the malicious nodes, the query-issuing node floods the MANET with a notification message including the signatures in which the identified malicious nodes replaced higher-score data items to their own lower-score items. Here, we assume that malicious node M 2 replaces the score data item sent by M 4 with its own score data item. The query-issuing node, M 1 , examines the reply message from M 2 , and it is clear from SIG M 2 that M 2 has replaced the score data item with its own low-score item.

Data management issues in mobile ad hoc networks

M 1 thus detects a DRA and identifies M 2 as a malicious node. The number of queries necessary for identifying all malicious nodes. Performance comparison of our methods. On the other hand, in MP-noSIG, even if k increases, the traffic required for notification is very small, as nodes send notification messages with only the information about identified malicious nodes i.

In summary, the simulation result shows the trade-off between quickness of malicious node detection achieved by our signature-based method in Ref. In real situations, we should carefully choose an appropriate method based on the system requirements i.

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In this section, as a summary of this paper, we first summarize the academic and social contributions of the studies. A number of the studies summarized above have been acknowledged as pioneering works, which together have established a new field of research, on data management in MANETs. The study described in Ref. After that initial study in Ref. Thus, our studies have contributed significantly to the advancement of academic research in this area.

Our studies also have significant social value, because technical advancement in MANET data management will significantly improve data availability and data access performance in important applications, such as those described in Section 1. As aforementioned, the techniques proposed in our studies have significant contributions from both the academic and social perspectives.

In the last part of this paper here, we discuss some future directions. While of course, performance improvement of each of the techniques proposed by us is needed, we omit such discussion, but focus on other research directions. In mobile crowdsensing, mobile devices with sensing capabilities act as sensor nodes and help sensing operations. In mobile crowdsourcing, mobile users act as workers to conduct some tasks which are part of a large mission.