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.

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.

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.

The opportunistic networks and the bike-sharing systems have been attracting much research attention in these years. In this paper, the cycling trips and the buffers of bike stations are utilized to relay the large volume of data. The Markov chain is exploited to formulate and estimate the delivery ratio with the mathematically lattice model. Then the Probability-driven Opportunistic Forwarding (POF) scheme, which calculates the delivery potentials from the possible delivery paths, is proposed. The relay evaluations, which consider the proper sequence of cycling trips, are made to support the data forwarding decisions. Moreover, the strategies, i.e., the discard notification and the backward elimination, are exploited to drop the redundant replications of data as many as possible. The extensive simulation results show that the proposed scheme outperforms several benchmark algorithms, e.g., Prophet, Spray&Wait and JDER, in terms of the delivery ratio, the delivery latency and the expected hop count.