Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle’s uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms ofuncertaintask delay and incurred payment. To maximize the requester’s utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop atruthfulbudgetedutilitymaximizationauction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.

Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices’ energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.

In cellular Internet of things (IoT) systems, massive low-power terminals update information status to cellular base stations to support diverse IoT applications. In this circumstance, information freshness and energy efficiency become two fundamental concerns. Except data transmissions, information updates consume additional energy for radio activation. To improve the energy efficiency, it is reasonable to aggregate the dynamically generated data. However, the reduced updates will severely deteriorate the information freshness, especially for time-critical IoT applications. To address this issue, we propose an upload scheduling scheme in this paper. Considering dynamic packet arrivals and channel conditions, the upload scheduling problem is formulated from a long-term perspective. To solve this problem, a practical online upload scheduler, named as FReshness-aware Energy efficient ScHeduler (FRESH), is proposed to minimize the update energy consumption subject to information freshness constraints. We theoretically show that FRESH can make the energy saving arbitrarily close to that of the optimal scheduling decision. Simulation results demonstrate the necessity and effectiveness of implementing FRESH for cellular IoT systems.

Given the fact that roughly 800 000 children are reported missing in the United States every year, how to assist parents to track their children becomes an important problem. Even though many children tracking systems have been proposed, the high cost and energy limitation of locators are the stumbling blocks which limit the application of those systems. To address this challenge, we design a children tracking system based on RFID technology, where children carry RFID tags and the system is responsible for locating the children by aggregating the readings from the deployed readers. Noting the importance of localized processing for efficient children tracking, we further study how the locally available computing resource, such as the mobile devices carried by the park employees and visitors, can be utilized for service provisioning. Since mobile devices have limited energy, we study an energy efficiency optimization problem by jointly considering the resource allocation and user association. The formulated problem is solved by a dynamic updating matching approach. Through extensive simulations, we have demonstrated the effectiveness of our proposed solution.

Mobile hotspots have made the dream of ubiquitous Internet access come true, while the widespread applications are still hindered by the limited power of smart phones. To address this issue, we propose a novel distributed cooperative data transmission scheme for energy-rechargeable mobile devices. In particular, we not only let a mobile phone help the nearby client devices connect to the Internet via its cellular accessing, but also let those clients replenish the mobile hotspot energy via wireless power transfer. We mathematically formulate the mutually beneficial relationship between mobile hotspots and clients into an optimization problem, with the objective of conducting the cooperative wireless data and energy transmission to maximize the system utility. Resorting to methods from combinatorics and matching theory, we develop a near optimal solution for many-to-one matching when there is a single mobile hotspot and a distributed matching strategy for the general case by considering the nature of data communication and the characteristic of wireless power transfer. By extensive simulation, we show that the proposed distributed solution achieves a performance close to the centralized method, and it outperforms the greedy matching strategy and the classic Gale-Shapley matching strategy in different scenarios.

Energy is the impediment to various applications of battery-powered wireless sensor networks (WSNs). Beyond the battery constraint of sensors/aggregation and forwarding nodes (AFNs), the major energy consumption of WSNs is from the longdistance multi-hop transmissions from the sensors/AFNs to the sink. To address these issues, in this paper, we employ a wireless charging vehicle (WCV) to travel inside WSNs to replenish the energy of sensors/AFNs, and cut long-distance transmissions into short-distance ones. Different from prior works, we let the WCV not only recharge the AFNs selectively, but also collect data from chosen AFNs and bring collected data back to the sink. The chosen AFNs play as virtual sinks, and nearby AFNs can use short-distance transmissions to deliver their traffic to the chosen AFNs. We formulate this problem into an energy replenishment optimization with joint consideration of sensed data delivery, flow routing, wireless power transfer, etc. Since the formulated problem is mixed integer nonlinear programming which is NP-hard to solve, we also develop a heuristic algorithm for feasible solutions. Through simulations, we show that the solution of the proposed algorithm is close to the optimal one and the energy replenishment is optimized while data delivery guaranteed.

Numerous applications of wireless sensor networks (WSNs) in harsh terrains are constrained by the sensors' battery-power and face the difficulties of data collection. In this paper, we propose to exploit wireless power transfer technology to replenish the energy of sensor clusters and develop an efficient data collection scheme for those wireless rechargeable senor clusters deployed in harsh terrains. In view of the harsh terrains, we employ unmanned aerial vehicles (UAVs) to travel to the sites of sensor clusters, collect data, and recharge the sensors in corresponding clusters. With joint consideration of data collection characteristics, wireless power transfer features and travel time, we mathematically formulate the data collection in rechargeable WSNs into an optimization problem with the objective of maximizing data collection utility. Based on the matching theory, we also develop a one side matching algorithm and a greedy algorithm to solve the problem in distributed manner. Through simulations, we show that UAVs are not always matched with nearest sensor clusters, the solution of the proposed greedy algorithm is optimal, and the sensed data can be efficiently collected.