To improve test data compression efficiency, the order of the scan unit need be adjusted, which indirectly changes the content of the test pattern. Scan chain partitioning is a common method that utilises this concept. However, current scan chain partitioning methods can still be optimised in terms of entropy and test data compression efficiency, and lack universality. To enhance the efficiency of code-based compression efficiency, we propose a scan slice reordering algorithm that minimizes entropy. This algorithm first calculates the rank of each scan slice or column vector of the test set, and then dynamically adjusts the order of the scan slices according to the descending order of these ranks in the pursuit of minimizing the entropy of the test set. By iterating through this process, the optimal position of each scan slice in the test set is ultimately determined. Compression experiments should be performed on all test patterns using different code-based schemes. This not only reduces the entropy of the test set but also significantly improves the efficiency of different codes. Compared to traditional scan chain partitioning method, FDR encoding achieved an average compression ratio increase of 6.16%, and RL-Huffman encoding achieved an average compression ratio increase of 4.96%. The experimental results demonstrate that our proposed algorithm is feasible, effective, and universal.

Fixed-wing unmanned aerial vehicle (UAV) communication is a promising paradigm for providing mobile video services to ground users (GUs) without the need of infrastructure. However, the performance of fixed-wing UAV video streaming is severely limited by the onboard energy while energy-efficient fixed-wing UAV video streaming has not been fully explored. In this paper, we study energy-efficient video streaming with a fixed-wing UAV for provisioning mobile video streaming services to multiple GUs. We define UAV's energy efficiency as the ratio of perceived video quality at all GUs to the UAV's energy consumption and formulate an energy efficiency maximization problem that jointly optimizes the communication time allocation among GUs and the UAV's trajectory. Due to the non-convex nature of the formulated problem, we propose a near-optimal iterative algorithm, which utilizes successive convex approximation and quadratic transform techniques to address the problem efficiently. Extensive simulations demonstrate the high efficiency and effectiveness of our proposed algorithm.

Computing power network (CPN), which integrates heterogeneous computing resources and communication network, can tackle the challenges brought by the pervasiveness of mobile and Internet of Things applications. In this paper, we study the optimization of task scheduling in a CPN network by considering the unbalancing between task distribution and resource cost. The design objective is to maximize the system profit while satisfying tasks' delay requirements. We formulate this problem as an integer programming problem. To address this NP-hard problem, we propose a Deep Reinforcement Learning (DRL) based multistage profit-aware task scheduling algorithm which first makes coarse grained task allocation using DRL among regions and then determines an optimized intra-region task assignment by using profit-aware balancing algorithm. Extensive simulations are conducted for performance evaluation and the results show the high performance of the proposed algorithm as compared with baseline algorithms.

In this paper, we study the design of opportunistic routing for efficient data offloading in vehicular opportunistic networks with the assistance of sparsely deployed static relays. The objective is to maximize the data offloading ratio and also reduce the average delivery delay while respecting the data’s delay requirement. For this purpose, we propose a sparse relay assisted opportunistic routing algorithm for efficient data offloading. We show how to efficiently utilize the encounters with static relays for improved data offloading performance based on vehicles’ trajectories. We further design a greedy algorithm for optimized relay deployment. We reduce the computational complexity of the data offloading algorithm and also the relay deployment algorithm, respectively. Simulation results demonstrate the high performance of our proposed algorithms.

Mobile video traffic has experienced explosive growth in recent years due to the rapid development of mobile intelligent terminals and cellular communication technologies. The rapid growth of mobile video traffic has brought significant energy expenditure for mobile network operators. To reduce the energy expenditure, one promising solution is to exploit renewable energy harvested from surrounding environments for cellular traffic delivery. In this article, we investigate mobile video streaming in green cellular networks with hybrid energy sources, i.e., grid energy and ambient energy, to optimize both video quality and energy expenditure. Specifically, we formulate a stochastic optimization problem to maximize the long-term time-averaged network service utility, which is the difference of video quality and energy expenditure. The problem formulation takes the following factors into account: time-varying grid electricity price, energy harvesting process, and different time scales of rate adaptation (RA), resource management, and electricity price fluctuation. We exploit Lyapunov optimization framework to decompose the problem into three subproblems: 1) RA subproblem; 2) battery energy management subproblem; and 3) joint power control and subchannel assignment subproblem. We propose an efficient online green video streaming algorithm to solve these subproblems. We analyze the stability of the proposed algorithm with respect to lengths of energy queue and user request queues. Extensive simulations are conducted and the results validate the efficiency of the proposed algorithm.

Mobile crowdsensing is a novel paradigm by exploiting mobility, sensing, computation, and communication capability of smart devices. In this paper, we study data offloading problem for mobile crowdsensing in opportunistic social networks. In this scenario, mobile users can upload sensing data directly via cellular networks using various data plans. A mobile user can also resort to another user for data offloading by forwarding sensing data to that user using short-range communications (when they encounter). To minimize total data uploading cost while meeting given uploading deadlines, data plan assignment for users and data forwarding strategy when two users encounter should be elaborately designed. In this paper, we use Benders decomposition algorithm to solve offline data plan assignment problem. Then we propose two algorithms including progress- balanced algorithm and social-aware forwarding algorithm to solve online data forwarding problem. Simulation results show that data offloading between users can largely reduce the total data uploading cost. Simulation results also show that the performance of our proposed online algorithms is close to the offline optimal solution.

Opportunistic routing has been a promising routing paradigm for the performance of a wireless sensor network (WSN) due to the broadcast and lossy characteristics of wireless channels. In this paper, we propose a Correlation Aware opportunistic Routing protocol CAR, which combines spatial correlation based data aggregation and opportunistic routing for achieving improved routing performance. The design behind CAR is to fully take advantage of the characteristic of spatial correlation among data sensed by neighbor sensor nodes and opportunistic nature for data reception in packet delivery in wireless channels. For this purpose, CAR first aggregate correlated data at selected forwarder and then opportunistically forward aggregated data toward the intended destination to reduce data redundancy. Extensive simulations show that CAR outperforms existing work in terms of data delivery cost and overall energy consumption.