Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for interested tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bidding-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted. In this paper, we design novel privacy-preserving incentive mechanisms to protect users’ true bid information against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated bids to the platform. Two solutions are proposed for the platform to solve the winner selection problem with the obfuscated information, which is proved to be NP-hard. Moreover, we further propose a novel task-bid pair protection truthful incentive mechanism to further prevent privacy leakage from the set of interested tasks, where each user encrypts his interested tasks via homomorphic encryption locally, and an encrypted task clustering method is proposed to group users with the same interested tasks into the same cluster for winner selection with users’ encrypted task-bid pairs. Both of theoretical analysis and extensive experiments demonstrate the effectiveness of proposed mechanisms against the untrusted platform.

With the widespread application of 5G and the Internet of things (IoT), edge computing and cloud computing have been collaboratively utilized for task offloading and processing. However, though the massive devices (e.g., smartphones) are organized into multi-cells, most of the existing works do not explore the computation offloading for edge-cloud computing under inter-cell interference. Thus, the offloading decisions may be inappropriate as the transmission rate is overestimated. To address this issue, we propose COMEC, a novel Computation Offloading scheme in Multi-cell networks with Edge-Cloud collaboration, which could minimize the total cost in terms of delay and energy consumption. Specifically, we first formulate COMEC as an optimization problem taking into account inter-cell interference. Then, considering the offloading decisions of all users are coupled, a non-cooperative game is formulated to minimize the total cost of each user in a distributed manner. We prove that this game is a general (ordinal) potential game and possesses a pure strategy Nash equilibrium (NE). Based on the finite improvement property of the potential game, we develop the corresponding computation offloading algorithm to achieve the NE. Finally, simulation results show that the proposed scheme can achieve superior performance in overall system cost compared with other baselines.

Recently, the newly emerging wireless powered communication network (WPCN) has drawn significant interests, where network nodes are powered by the energy harvested from the radio-frequency (RF) signal. This paper studies the WPCN where one hybrid sink (H-sink) coordinates the wireless energy/information transmissions to/from a set of one-hop nodes powered by the harvested RF energy only. The transmission completion time (TCT) minimization for the uplink (UL) transmissions of a given number of bits per node is considered. First, we prove that the harvest-then-transmit (HTT) transmission strategy is one of the transmission strategies able to achieve the minimal TCT, where all nodes first harvest the RF energy broadcast by the H-sink in the downlink and then send their independent information to the H-sink in the UL by time-division multiple access. Then for the HTT transmission, we prove that in order to achieve the minimal TCT, each node must transmit with constant power and consume all available energy, which helps to simplify the considered TCT minimization problem to be the optimization of time allocated for the H-sink's wireless energy transfer and the nodes' wireless information transmissions, and we formulate the optimal time allocation problem as a nonlinear optimization problem. Finally, we prove that it is a convex optimization problem. Due to the inexistence of explicit closed-form expressions of optimal time allocations to minimize TCT, one efficient algorithm is presented to obtain the optimal time allocations. Simulation results show that, compared with the available transmission strategies, the designed TCT-minimized transmission achieves a significantly smaller TCT.

Narrowband internet of things (NB-IoT) is a new cellular technology introduced by the Third Generation Partnership (3GPP) providing low-power and wide-area coverage for IoT. In this work, we consider the scenario that NB-IoT is deployed in an heterogeneous network and the quality of the direct link from the NB-IoT user equipment (UE) to the serving base station (BS) cannot satisfy the quality of service (QoS) requirement for transmission of vital sensing data. Thereupon, device-to-device (D2D) communication is adopted as a routing extension to NB-IoT systems and thus enables two-hop routes between NB-IoT user equipment (UE) and the serving BS via a set of D2D relays. As the candidate UE relays work in duty cycle to save energy, we derive a model to select a set of UE relays and perform opportunistic D2D communication according to a working schedule. Two optimization problems are formulated aiming at achieving optimal expected delivery ratio and expected two-hop delay, respectively. Dynamic programming-based algorithms are proposed to solve the optimization problems and obtain the optimal working schedule of the relays. Simulation results demonstrate that our proposed maxEDR and minEED algorithms improves the system performance compared with other state-of-the-art algorithms.

With the promising technology of far-field wireless power transfer, wearable devices are able to harvest radio frequency (RF) energy from RF-based chargers and thus operate continuously. Rational planning of the quantity and positions of the chargers provides an effective way to improve the recharging efficiency and save the charger deployment budget. Existing work on RF-based charger placement mainly considers the situation where the devices are static, or adopt very simple mobility model. In this paper, we consider wireless charging service provision for wearable devices worn by users in a 2-D area, with users having a specific stay-move behavior pattern characterized by the trajectories, stay points and stay time distribution. Based on this mobility pattern, we formulate the problem as how to find a charger placement to minimize the charging service budget, subject to the power non-outage probability requirement of the wearable devices. We further transform this problem to several equivalent problems for easier tractability and prove that it is NP-complete. Both greedy heuristic and particle swarm optimization (PSO)-based solutions are proposed to solve the problem with certain approximation ratio. Finally, performances of the proposed solutions are compared with exhaustive search and existing point provisioning algorithm through extensive simulations. Simulation results show that both greedy heuristic and PSO-based solutions outperform the baseline algorithms, while PSO-based solution requires least number of chargers and thus is the most cost-effective.

RFID systems can be applied to efficiently identify the missing items by attaching them with tags. Prior missing tag identification protocols concentrated on identifying all of the tags. However, there may be some scenarios in which we just care about the key tags instead of all tags, making it inefficient to merely identify the missing key tags due to the interference of replies from the ordinary tags (i.e., non-key tags). In this paper, we propose to investigate the problem of efficiently and completely identifying the missing key tags for anonymous RFID systems in which the tag privacy is required to be well protected. Firstly we propose a VEctor-based missing Key tag Identification protocol called VEKI. Then we propose an improved protocol called iVEKI, which consists of two phases: ordinary tag deactivation and missing key tag identification. The parameters of the proposed VEKI and iVEKI protocols are theoretically optimized to maximize the time efficiency. Finally we conduct extensive simulations to evaluate the proposed VEKI and iVEKI protocols and the simulation results illustrate that they outperform other existing protocols in terms of execution time.