At present, mobile charging stations (MCSs) are taken as an important complement of fixed charging stations. Currently, the strategy of MCSs is to move towards the electric vehicles to be charged (EVCs) only after being requested. To shorten the charging delay of EVCs and enhance the proportion of charged EVCs, idle MCSs should actively move to the areas with large potential charging demand rather than remaining stationary. The distribution of idle MCSs in different areas should be taken into account to prevent excessive idle MCSs from moving into the same areas simultaneously. To this end, we introduce the concept of charging demand force to depict the potential charging demand of EVCs, and then propose the Placement Strategy for Idle Mobile Charging Stations (PS-IMCS). In PS-IMCS, each idle MCS can measure the potential charging demand in neighboring areas through obtaining the resultant force composed of attraction force and repulsion force, and an MDP model is specially designed to make placement decisions for idle MCSs. Extensive simulations and comparisons demonstrate the performance superiority of PS-IMCS, i.e., the charging delay of EVCs can be significantly shortened, and the proportion of charged EVCs can be effectively enhanced.

Abstract Delay tolerant networks (DTNs) refer to a kind of novel wireless mobile network, where there is no constant end‐to‐end connection between network nodes due to frequent movement, sparse distribution and limited communication range of nodes. Instead of the traditional store‐forward routing strategy, in DTNs, the new store‐carry‐forward routing strategy is adopted for data transmission. Therefore, how to select the best next‐hop node among network nodes is the main challenge of the routing in DTNs. To this end, here, a k ‐step double Q‐learning routing (K‐DQLR) algorithm is proposed, which integrates the multi‐step and double Q‐learning algorithms to make an unbiased, accurate and efficient routing decision in DTNs. Besides, a new dynamic reward mechanism is proposed, which combines the number of routing hops and the node centrality to adopt the dynamic network environment of DTNs. The simulation results show that K‐DQLR can significantly increase the delivery ratio while reducing the delivery delay and overhead compared with the related state‐of‐the‐art routing protocols of DTNs.

The electric vehicles (EVs), as promising components of sustainable and eco-friendly transportation systems, are being widely adopted to reduce the consumption of fossil fuel and the pollution of environments. EVs are usually equipped with wireless communication modules to support the vehicle to vehicle (V2V) communications, and thus an electric vehicular network (EVN) is constituted. However, the electric energy of EVs is extremely limited, and some EVs are possible to exhaust their electric energy before reaching the destinations. More seriously, when the EVs travel into the zones without any charging stations, they cannot be timely charged. With the rapid developments of wireless charging technologies (such as magnetic resonance) and graphene supercapacitor technologies, the reciprocal charges between EVs become feasible, i.e., the EVs with insufficient energy (IEVs) can be charged by the EVs with surplus energy (SEVs). In this paper, the strategies of selecting the local-optimal SEVs for IEVs and rescheduling their travel routes are investigated, and a distributed Reciprocal Charging Mechanism (RCM) is proposed. Both mechanism analysis and simulation results demonstrate the performance superiority of RCM. Specifically, with the proposed reciprocal charging mechanism, IEVs can be charged by SEVs in a charging-station-absent zone, and the electric energy consumption can be approximatively minimized.

The movement of a cruising ET (which do not pick up passengers) is quite sophisticated, and it depends on the driver’s subconscious movement tendency, driving habits, and historical pick-up experiences. This paper exploits the links between ETs and charging stations according to historical trajectories, and proposes a Charging station Recommendation Algorithm based on Link Prediction (CRA-LP) to recommend the optimal charging stations for cruising ETs. Specially, in CRA-LP, the Adamic-Adar index with a time decay effect is given to measure the similarities between ETs and charging stations. Simulations results show that CRA-LP achieves preferable results in terms of some metrics, such as Area Under Curve and Average Extra Movement, which also indicates that CRA-LP can predict the future links between cruising ETs and charging stations accurately, and then recommend the proper charging stations to cruising ETs to minimize the extra movements.

Abstract Opportunistic underwater sensor network (OUSN) is deployed for various underwater applications, such as underwater creatures tracking and tactical surveillance. Particularly, the OUSN in military applications may be invaded by some underwater spy‐robots termed eavesdroppers. The eavesdroppers could move around some OUSN nodes and eavesdrop on their communication channels silently, and these eavesdropping actions are difficult to be perceived by OUSN nodes. To reduce the theft ratio of data messages and guarantee the required delivery ratio of data messages, we conceive the idea that each data message is encoded into several message pieces, and then the message pieces are disseminated to sink node individually. Besides, a lightweight encryption method is adopted to encrypt the message pieces before the dissemination. Such mechanism can protect the data messages from being stolen by eavesdroppers effectively. In this article, we propose a message piece dissemination approach (MPDA) for the OUSN invaded by some underwater spy‐robots. In MPDA, OUSN nodes disseminate the held message pieces to some selected neighboring nodes at each time slot, and a data message is considered to be delivered when all pieces of this data message have been delivered to the sink node. Extensive simulations and comparisons demonstrate the preferable performance of MPDA, that is, MPDA can reduce the theft ratio of data messages and guarantee the required delivery ratio of data messages.

Opportunistic Underwater Sensor Networks (OUSNs) are deployed for various underwater applications, such as underwater creature tracking and tactical surveillance. In an OUSN invaded by some eavesdroppers, the data messages disseminated by sensor nodes are probably stolen (captured and cracked) by the eavesdroppers. The data messages are disseminated through acoustic waves which could be altered by the environmental noises, i.e., the acoustic waves containing data messages could be superimposed by the environmental noises. To protect the data messages from being stolen by eavesdroppers and guarantee the required delivery ratio of data messages, we propose a Noise-based-protection Message Dissemination Method (NMDM). In NMDM, the acoustic waves containing data messages are superposed by the environmental noises and converted into some pseudo data messages. The environmental noises around source nodes are identified, encoded, and encrypted into some noise messages. Then, the pseudo data messages and noise messages are individually disseminated to the sink node. Such mechanism makes the eavesdroppers difficult to steal the data messages. Besides, the required delivery ratio of data messages is achieved by measuring the similarities between the nodes and the sink node, i.e., the pseudo data messages and noise messages are preferentially disseminated to the nodes with larger similarities to the sink node. Finally, simulation results demonstrate the superior performance of NMDM. NMDM can reduce the theft ratio of data messages and guarantee the required delivery ratio of data messages effectively.

Electric vehicles (EVs), as promising components of the sustainable and eco-friendly transportation systems, are being widely adopted to reduce the consumption of fossil fuel and pollution of environments. EVs are usually equipped with wireless modules to support the vehicle to vehicle communications, by which an electric vehicular network (EVN) is formed. In EVN, some EVs are with insufficient battery energy and may exhaust the battery energy before arriving at their destinations, and these EVs are referred to as IEVs. More seriously, IEVs probably cannot find any fixed charging facilities nearby. With the development of mobile charging technology, some movable charging stations (MCSs) are deployed into EVN, and MCSs can actively navigate to charge IEVs. In this paper, an assignment rescheduling mechanism of movable charging stations (ARMM) is proposed, where the MCS assignments are dynamically rescheduled. In ARMM, in order to reduce the charging expenses of IEVs and enhance the proportion of charged IEVs, the assigned IEVs of some MCSs could be switched to other MCSs, while the charging positions of MCSs are selected by minimizing the charging expenses of IEVs and are dynamically altered. Besides, the incentives of assigned IEVs to reduce the charging expenses of unassigned IEVs are proven. Simulation results demonstrate the preferable performance of ARMM, i.e. ARMM can reduce the charging expenses of IEVs and enhance the proportion of charged IEVs effectively.

Opportunistic underwater sensor networks (OUSNs) are deployed for various underwater applications, such as underwater creatures tracking and tactical surveillance. The data dissemination in OUSNs differs significantly from those in terrestrial wireless sensor networks or delay-tolerant networks, due to the signal irregularity in underwater communications and the limited storage capacity of the nodes in OUSNs. To alleviate the storage overflows on nodes and make room for the newly arriving data packets, some stored data packets ought to be actively discarded by nodes. This research begins with the construction of a differential equation set to describe the propagation process of data packets in OUSNs, and the storing-discarding equilibrium is investigated such that each data packet is expected to propagate and disappear during the allowable dissemination time slots. After that, the optimal storing probabilities and discarding probabilities are obtained for the nodes with different in-degrees to maximize the delivery ratio of data packets. Then, we propose an Adaptive Data Dissemination Algorithm (ADDA) for the storage-limited OUSNs with signal irregularity, where at each time slot the newly arriving data packets are stored and the stored data packets are discarded by nodes according to the obtained storing probabilities and discarding probabilities, respectively. Simulation results demonstrate the excellent performance of ADDA, showing that it can enhance the delivery ratio of data packets and reduce the number of storage overflows.

Abstract The technique of vehicle license plate recognition can recognize and count the vehicles automatically, and thus many applications regarding the vehicles are greatly facilitated. However, the recognitions of vehicle license plates are extremely difficult especially in some fog‐haze environments because the fog and haze blur the boundaries and characters of license plates significantly, which makes the license plates hard to be detected or recognised. To this end, this paper proposes a vehicle License Plate Recognition method for Fog‐Haze environments (LPRFH). In LPRFH, a dark channel prior algorithm based on the local estimation of atmospheric light value is applied to dehaze the blurred images preliminarily. Then, the images are further dehazed, and the license plate regions are detected through a Joint Further‐dehazing and Region‐extracting Model on basis of an object detection convolution neural network. Finally, the image super‐resolution is accomplished with a convolution‐enhanced super‐resolution convolutional neural network, and hence the characters of license plates can be recognised successfully. Extensive experiments have been conducted, and the results indicate that LPRFH can recognise the license plates accurately even in some severe fog‐haze environments.

Underwater acoustic sensor networks (UASNs) have attracted considerable attention due to the increasing demands on the Internet of Underwater Things. The monitored underwater data are disseminated by anchored nodes in UASNs through multihop transmissions. In the military applications, some underwater spy robots are probably dispatched by the enemy to invade the UASNs, and these underwater spy robots seek to steal the data messages disseminated by anchored nodes. In this article, the autonomous underwater vehicles (AUVs) are taken as the ferries for the data message dissemination of some vulnerable anchored nodes, so as to reduce the risk of data messages being stolen by the spy robots. To this end, an index dissemination vulnerability is first introduced to measure the risk of data messages being stolen around anchored nodes, and the network topology is specially investigated to ameliorate the dissemination vulnerabilities of anchored nodes. An AUV-aided message ferry approach (AMFA) is proposed for the UASNs invaded by underwater spy robots. In AMFA, the anchored nodes set the initial communication ranges according to a power law distribution, and then, the communication ranges of anchored nodes are updated to achieve the required topology connectivity. Especially, some AUVs are assigned to ferry the data messages of the anchored nodes with the largest dissemination vulnerabilities. The simulation results demonstrate the preferable performance of AMFA, i.e., AMFA ameliorates the dissemination vulnerabilities of anchored nodes considerably while the required topology connectivity can be guaranteed.

Delay‐tolerant networks (DTNs) are wireless mobile networks, which suffer from frequent disruption, high latency, and lack of a complete path from source to destination. The intermittent connectivity in DTNs makes it difficult to efficiently deliver messages. Research results have shown that the routing protocol based on reinforcement learning can achieve a reasonable balance between routing performance and cost. However, due to the complexity, dynamics, and uncertainty of the characteristics of nodes in DTNs, providing a reliable multihop routing in DTNs is still a particular challenge. In this paper, we propose a Fuzzy‐logic‐based Double Q‐Learning Routing (FDQLR) protocol that can learn the optimal route by combining fuzzy logic with the Double Q‐Learning algorithm. In this protocol, a fuzzy dynamic reward mechanism is proposed, and it uses fuzzy logic to comprehensively evaluate the characteristics of nodes including node activity, contact interval, and movement speed. Furthermore, a hot zone drop mechanism and a drop mechanism are proposed, which can improve the efficiency of message forwarding and buffer management of the node. The simulation results show that the fuzzy logic can improve the performance of the FDQLR protocol in terms of delivery ratio, delivery delay, and overhead. In particular, compared with other related routing protocols of DTNs, the FDQLR protocol can achieve the highest delivery ratio and the lowest overhead.

As an important issue in the trajectory mining task, the trajectory clustering technique has attracted lots of the attention in the field of data mining. Trajectory clustering technique identifies the similar trajectories (or trajectory segments) and classifies them into the several clusters which can reveal the potential movement behaviors of nodes. At present, most of the existing trajectory clustering methods focus on some spatial properties of trajectories (such as geographic locations, movement directions), while the spatial-temporal properties (especially the combination of spatial distances and semantic distances) are ignored, and thus some vital information regarding the movement behaviors of nodes is probably lost in the trajectory clustering results. In this paper, we propose a Joint Spatial-Temporal Trajectory Clustering Method (JSTTCM), where some spatialtemporal properties of the trajectories are exploited to cluster the trajectory segments. Finally, the number of clusters and the silhouette coefficient are observed through simulations, and the results show that JSTTCM can cluster the trajectory segments appropriately.

The opportunistic mobile sensor network has been extensively applied in various public safety applications such as the fire rescue and earthquake rescue, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of humans staying in risk zones. However, due to some environmental events such as building structure damage, airflow push, and fire explosions, the sensor nodes sprinkled into the fire-rescue scenario may be kept moving. Thus, the contacts between nodes become momentary, and the data packets cannot be forwarded along stable communication paths. To this end, the opportunistic forwarding manner is adopted in the fire-rescue scenario to enable the data packets to be transferred to the rescue control center (RCC) through some discrete hops. The contributions of this paper are threefold. First, the nodes in the fire-rescue scenario are carefully investigated and classified into four types: small-range mobile nodes (SRNs), large-range mobile nodes (LRNs), firefighter nodes (FNs), and robot nodes (RNs). Second, we formulate the data forwarding problem, and the optimal proportions of SRNs, LRNs, and FNs in data holders are mathematically analyzed to obtain the maximum delivery ratio. Third, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, the optimal proportions of SRNs, LRNs, and FNs in data holders are maintained as far as possible through selecting different types of data holder candidates, and then the new data holders are determined from these data holder candidates and the adjacent RNs on basis of their expected delivery delay. Finally, the performance of DFAFR is analyzed through simulations of the fire-rescue scenario, and the results indicate that DFAFR can enhance the delivery ratio and shorten the delivery delay while the forwarding overhead is restricted.

Trajectory data mining has become an increasing concern in the location-based applications, and the trajectory partition is taken as the primary procedure of trajectory data mining. The amount of movement trajectories of nodes is typically very large, and the trajectory shapes are extremely diverse, which makes the trajectory partition a vital issue to the trajectory data mining results. In this work, the movement behaviors of nodes are analyzed from the aspects of moving speeds, stop points, and moving directions, and then a novel Trajectory Partition Method based on combined movement Features (TPMF) is proposed to partition the trajectories. In TPMF, we first extract the change points where the movement speeds of nodes are varied significantly; then, we extract the stop points by detecting the speed variations of nodes; finally, the Douglas-Peucker algorithm is applied to partition the subtrajectories according to the extracted feature points (change points and stop points). Simulations are carried out on the Geolife trajectory dataset, and the simulation results indicate that TPMF can achieve a preferable trade-off between the simplification rate and the trajectory partition error, while the running time is shortened as well.

The opportunistic mobile sensor network has been extensively used in various public safety applications such as the fire-rescue scenario, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of staying in the risk zones to humans. However, the sensor nodes thrown by firefighters in the fire-rescue scenario are easy to move away from current positions due to many environmental factors such as the building structure damages, airflow push or even some explosions. Consequently, the contacts between nodes become scarce and momentary, thereby making the gathered data packets difficult to be forwarded along stable communication paths. Firstly, the mobility patterns of nodes in the fire-rescue scenario are classified into three types: small-range mobile nodes, large-range mobile nodes and firefighter nodes. Then, the optimal proportions of different types of nodes in the data holders are specially investigated mathematically to maximize the delivery ratio. Thus, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, each data holder forwards the held data packets to neighbouring nodes independently, and the optimal proportions of data holders are maintained approximatively. Finally, the performance of DFAFR is analyzed through simulation experiments that produce preferable results in the fire-rescue scenario, indicating that DFAFR can improve the delivery ratio and shorten the delivery delay, so that the fire behavior can be reported and processed timely.

Delay-tolerant networks (DTNs) are wireless mobile networks where constant end-to-end connections may not exist among nodes. In real-life vehicle DTNs, most nodes have repetitive movement patterns. However, due to the change of time and different activity scenarios, the movement patterns cannot be described consistently with a single model. Considering this issue, the Multi-period Bayesian Network (MBN) is proposed to build multiple prediction models, which intends to predict the regular movement patterns of nodes in the real world. The Bayesian network model is constructed by using several network parameters (e.g. spatial and temporal information at the time of message forwarding) to describe the movement patterns of DTN nodes. Additionally, a novel classification method called Dynamic Multiple-Level Classification (DMLC), is proposed where nodes are classified into multiple levels according to the dynamic parameters. Followed by that, a routing algorithm based on MBN is presented, which can make routing decisions based on the classification results of DMLC. The simulation results show that MBN algorithm and DMLC method can improve the delivery ratio with a minor forwarding overhead.

Delay-tolerant networks (DTNs) are wireless mobile networks that exhibit frequent intermittent connectivity and large transmission delay among nodes. Research results have shown that quota-controlled routing protocols can strike a reasonable balance between routing performance and cost, where quota is a value to control the number of message copies. However, the question of how to set the optimal quota dynamically in order to achieve the lower bound of routing cost is still open. In this paper, we model the optimization of quota control as an extremal functional problem and analyze it by a classic mathematical method called Calculus of Variations (CoV) for the first time. The function of time-variant quota with minimal average number of message copies is obtained in closed form, and an optimal quota control algorithm is proposed under practical routing design considerations. Both the numerical and simulation results show that the proposed model and algorithm are effective and efficient.

Delay-Tolerant Networks (DTNs) are wireless mobile networks, where the nodes are sparse and end-to-end connectivity is rare. Since DTN nodes are mostly energy-limited devices, there is an immediate need to have energy-efficient routing protocols, allowing the network to perform better and function longer. Besides, in the real world, people carrying the nodes form a lot of communities because of similar interests, and they behave with social selfishness. How to improve the energy efficiency in multi-community scenarios has been an important problem. In this paper, we analytically model the performance of epidemic routing protocols in multi-community scenarios with social selfishness considerations using the Ordinary Differential Equations (ODEs). Further, an energy-efficient copy-limit-optimized algorithm based on the Box's complex method for epidemic routing is proposed, which is designed to determine the optimal copy limit in multiple communities, and can improve the energy efficiency effectively. At last, both the numerical and simulation results show that the routing protocol with the proposed algorithm can reduce the energy consumption effectively, and the impact of social selfishness is also analyzed.

Node mobility and end-to-end disconnections in Delay Tolerant Networks (DTNs) greatly weaken the effectiveness of data transmission. Although social-based strategies can be used to deal with the problem, most existing approaches adopt multicopy strategy to forward messages which inevitably add more unnecessary cost. One of the most important issues is the selection of the best intermediate node to forward messages to the destination node. In this paper, we focus on finding a quality metric associated with better relays which is evaluated by Reachable Probability Centrality (RPC) as we proposed. RPC combines the contact matrix and multi-hop forwarding probability based on the weighted social network, thus ensuring an effective relay selection. We also propose a distributed RPC-based routing algorithm, which demonstrates the applicability of our scheme in the decentralized environment of DTNs. Extensive trace-driven simulations show that RPC outperforms other centrality measures and our proposed routing algorithm can significantly reduce the data forwarding cost while having comparable delivery ratio and delay to those of the Epidemic routing.