Opportunistic Networks (OppNets) can provide a low-cost and reliable way for the message forwarding in urban areas, especially in which traffic jams occur frequently. However, in terms of the OppNet based on the bicycle-sharing system (BSS), how to predict bicycle trips and improve routing performance still remains unsolved. Moreover, the exchange of auxiliary information among OppNet nodes (bike stations) will compromise the privacy of nodes/users. Thus we design the Two-Tier Probability Model (TTPM), including the InteR-day pattern and the IntrA-day pattern, to predict the trips accurately. Then the Discrete Optimization Differential Privacy (DODP) method is utilized to disturb the estimated InteR-day and IntrA-day probabilities, which will further protect the privacy of nodes and users. With TTPM and DODP, we propose an efficient privacy-preserving routing scheme for OppNet, which transforms the relay selection problem into the shortest path problem approximately. Extensive simulations show that the proposed routing scheme (TTPM) outperforms the benchmarks with the delivery ratio of more than 0.75 when the time-to-live is 5 days and the message generation rate is 6 pkts/hour. Compared with TTPM+Lap and TTPM+GRR, the proposed TTPM+DODP improves the delivery ratio by 30% and 3%, respectively.

Selfishness detection offers an effective way to mitigate the routing performance degradation caused by selfish behaviors in Opportunistic Networks but leads to extra network traffic and computational burden. Most existing efforts focus on designing the selfishness detection scheme by exploiting the behavioral records of nodes. In this paper, we investigate the scheduling strategy of selfishness detection during the message lifespan with the game theory. Specifically, the Long-term Selfishness Detection Game (LSDG) is proposed based on the differential game and the payoff in the integral form. LSDG formulates the selfishness detection and the node’s selfishness with the Ordinary Differential Equations (ODEs). Then, we prove the existence of the Nash equilibrium in LSDG and deduce the necessary conditions of the equilibrium strategy based on Pontryagin’s maximum principle. The recursion-based algorithm is designed in this paper to compute the numerical solution of the equilibrium strategy via Euler’s method. Both the soundness of our modeling approach and solution properties are verified by extensive experiments. The simulations also show that the obtained solution can achieve the Nash equilibrium, where neither the source node nor relay nodes can benefit more by solely changing their own strategies.

As a promising network architecture, Mobile Edge Computing (MEC), has been proven that can effectively reduce the end-to-end latency and the energy consumption. The Unmanned Aerial Vehicle (UAV) assisted MEC network, where the UAV can provide the computation offloading services for the mobile users, can further alleviate the huge deployment cost of static edge servers. However, it remains unsolved how multiple cooperative flying UAVs serve the ground users, especially considering that these UAVs may share the same wireless channel and can communicate with the users while flying. In this paper, we first propose the Age of Task (AoT) metric to measure the quality of experience, and then formulate the joint optimization problem to minimize the worst AoT among all the users. Based on the block coordinate descent (BCD) method, this problem is transformed into three non-convex programming sub-problems (i.e., the UAV-user association sub-problem, the UAV trajectory planning sub-problem and the transmit power optimization sub-problem). Specifically, the successive convex approximation (SCA) technique is exploited iteratively to deal with the non-convexity in the UAV trajectory and transmit power optimization. Numerical results show that the proposed scheme outperforms the benchmark offloading schemes in terms of AoT.

Unmanned Aerial Vehicles (UAVs) have been drawing significant attention as effective tools for observing disasters, especially in the context of large-scale disasters, where the establishment of seamless communication channels becomes imperative for verifying safety details and assessing the extent of the disaster. In most instances, insufficient UAV inventories make collaborative efforts among UAVs indispensable for enabling rapid and long-distance data transmission. This paper introduces a UAV data collection model designed for relay transmission of observed data through multi-hop relay communication. It also explores the freshness of data delivery, quantified using the recently developed Age of Information (AoI). Additionally, we derive the formula for calculating AoI and propose a UAV flight scheduling strategy aimed at enhancing the long-term average instantaneous AoI. We demonstrate the performance benefits of this strategy in terms of AoI, average duration, and energy consumption under the proposed framework through a comparison with traditional strategies.

Nowadays, the flourish of the internet of things incurs a great demand for progressive technologies to prolong the lifetime of Wireless Sensor Networks. Exploiting a fleet of Mobile Chargers (MCs) to replenish the energy-critical sensor nodes provides a new dimension to maintain long-term network operations, but may suffer from high charging delay due to MC’s limited mobility. Most existing studies focus on the reduction of server-oriented delay, i.e., the overall time taken by MCs (servers) to carry out sensor charging and travel inside the sensing field. However, these solutions may not be robust enough as some energy-critical sensor nodes will run out of the stored energy before the charger’s arrival. In this article, we address this challenge by reducing the client-oriented delay—referred to as the wait-for delay —which is defined as the “arrival times” at the to-be-charged sensor nodes (clients). To this end, we first formulate a novel wait-for charging delay minimization problem under the multi-node energy charging scheme. We then prove the NP-hardness of the proposed problem. Inspired by empirical observations, we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We have evaluated the proposed algorithm using real-life system settings. The experimental results suggest that the proposed algorithm certainly performs better than the existing benchmarks; it could reduce the wait-for delay by up to 87.4 percent.

Selfish node detection offers an effective means to mitigate the routing performance degradation caused by selfish behaviors in opportunistic Networks (OppNets), but leads to the extra network overload and computation cost. Most existing effort in the literature focuses on exploring the detection methods based on the traffic analysis or the cooperation among nodes. In this paper, we investigate the state transition of nodes in the message dissemination without detection. Specifically, the Ordinary Differential Equation (ODE) is constructed to approximatively model the periodic detection with complete detection requirements. Then we obtain the optimal solution of the selfish node detection by the Pontryagin’s maximum principle, and mathematically deduce the right detection time during the message lifetime. The model soundness is verified statistically and the analysis accuracy is evaluated via extensive simulations. The experiments also show that our solution can achieve the tradeoff between the reward and the detection cost.

Blackhole detection in the opportunistic networks offers an effective means to mitigate the routing performance degradation but faces many challenges from corrupted nodes due to their collusion behaviors. Most existing effort in the literature focuses on the blackhole feature extraction from the message exchange. However, the decay effect of features and the forged features from the corrupted node, which acts as the rational node in performing message exchange, degrade the performance of the detection. In this article, we investigate the evidence construction, i.e., the direct and indirect evidence with the statistical parameters in message exchange. Specifically, we construct behavior classifiers to distinguish the blackhole behaviors from rational ones and design the collusion filtering strategy to improve the detection accuracy by separating corrupted nodes from rational ones, laying a behavior identification foundation. The contact evidence-driven blackhole detection (CEBD) based on machine learning is proposed to improve the routing performance. The soundness of the proposed scheme is verified statistically and the detection accuracy is evaluated based on random waypoint model (RWP) trace and Shanghai taxi trace. Extensive simulations show that our scheme outperforms the benchmarks, including SDBG, Li, and MDS, in terms of the delivery ratio in various scenarios.

The campus Wi-Fi access trace is usually recorded when the users log in the campus Wi-Fi and access Internet. The network traffic, which records the users' network access information after log in successfully, e.g., source/destination IP, URL address, packet size, access time, is utilized to perform users profiling to figure out the campus users. In this paper, we utilize the network access trace and the network traffic in SEU university to profile the campus users. Here the Wi-Fi access records from wireless APs can be regarded as the mobility behaviors of users in the campus. The network traffic, which will be classified into several categories first, is quantified with the temporal dimension. With these two network datasets, we propose a Conditioned Reclassifying Algorithm based on BPNN, CRAB algorithm, to distinguish the faculty members from the students. Then the graduates and the undergraduates are identified through the binary classifying approaches. The disciplines of graduates are predicted with multi-classification approaches. Finally, the performances of the user identification prediction, i.e., Faculty/Student, Graduate/Undergraduate, and the discipline prediction of graduates are evaluated in terms of accuracy, precision and recall. Experimental results validate the effectiveness of our profiling method.

In order to alleviate the traffic congestion caused by the bike-sharing system, the bicycles should be parked in designated parking sites, particularly around the hot scenic spots. A proper route, which guides the cyclers to select a vacant place among the sites to park the bike, is required. In this paper, the Expected Travel Distance (ETD) and the Probability of Successful Parking (PSP) are formulated to evaluate the routes, which will guide the users to travel all the parking sites. We exploit the Poisson process to model the increment of the bicycle number in the parking site and construct the travel tree for the route plan problem. To provide a proper route, we propose the GOR algorithm and the F-M method based on the travel tree. Through extensive simulations, our algorithms are compared with TSP in terms of ETD, PSP and the execution time.

Exploiting community structure for opportunistic forwarding decisions in mobile social networks offers a promising paradigm to improve the transmission performance and reduce the extra network overhead. Actually, people will have closer relationships and more opportunities to contact with each other if they are in the same community. In this paper, the activeness of nodes and the probability of reaching the destination are investigated based on the node contacts in the trace. Then the Contacts-Aware Opportunistic Forwarding (CAOF) scheme, which includes inter-community and intra-community phase, is proposed. In the inter-community phase, the node with higher global activeness and source-to-destination probability is selected to serve as the relay. Besides, in the intra-community phase, the forwarding decisions are determined by the local metrics. Furthermore, we compare the proposed CAOF scheme with several benchmark forwarding algorithms, including BUBBLE Rap, SPRINT, Epidemic and JDER. The validity of the modeling and the soundness of the analysis are verified through extensive experiments with real traces, which illustrates that it outperforms other routing strategies in heavy traffic scenarios.

Worker selection for location-based crowdsensing can be described as the strategy of choosing the proper cooperative participants to complete the allocated tasks in specified regions. Due to the mobility pattern of vehicles and regular road networks, the Vehicular Ad-hoc Networks (VANETs) are expected to provide many opportunities for task execution in opportunistic crowdsensing, enabling some emerging applications. To fulfill tasks with the least execution time under the spatial-temporal restrictions, we propose a Location-Aware Worker Selection scheme (LAWS) for mobile opportunistic crowdsensing in urban areas. Different from the traditional worker selection schemes assigning a task to one designated worker, LAWS exploits the vehicles contacts provided by taxicabs and buses and makes full advantage of prior knowledge of vehicles to promote the performance of task execution. Real-world vehicle traces are introduced to construct the extensive simulations. The simulation results show that our scheme outperforms the well-known algorithms, e.g., Epidemic, Prophet, in terms of the task execution success ratio, the execution time and the network load.