Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications

XiaojingChen, Wei Ni, Xin Wang, YichuangSun, “Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications”, IEEE Transactions on Wireless Communications, Vol. 15 (5): 3269-3280, January 2016. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of O(N2) in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm.Peer reviewedFinal Accepted Versio

Energy Harvesting Broadband Communication Systems with Processing Energy Cost

Communication over a broadband fading channel powered by an energy harvesting transmitter is studied. Assuming non-causal knowledge of energy/data arrivals and channel gains, optimal transmission schemes are identified by taking into account the energy cost of the processing circuitry as well as the transmission energy. A constant processing cost for each active sub-channel is assumed. Three different system objectives are considered: i) throughput maximization, in which the total amount of transmitted data by a deadline is maximized for a backlogged transmitter with a finite capacity battery; ii) energy maximization, in which the remaining energy in an infinite capacity battery by a deadline is maximized such that all the arriving data packets are delivered; iii) transmission completion time minimization, in which the delivery time of all the arriving data packets is minimized assuming infinite size battery. For each objective, a convex optimization problem is formulated, the properties of the optimal transmission policies are identified, and an algorithm which computes an optimal transmission policy is proposed. Finally, based on the insights gained from the offline optimizations, low-complexity online algorithms performing close to the optimal dynamic programming solution for the throughput and energy maximization problems are developed under the assumption that the energy/data arrivals and channel states are known causally at the transmitter.Comment: published in IEEE Transactions on Wireless Communication

Timely-Throughput Optimal Scheduling with Prediction

Motivated by the increasing importance of providing delay-guaranteed services in general computing and communication systems, and the recent wide adoption of learning and prediction in network control, in this work, we consider a general stochastic single-server multi-user system and investigate the fundamental benefit of predictive scheduling in improving timely-throughput, being the rate of packets that are delivered to destinations before their deadlines. By adopting an error rate-based prediction model, we first derive a Markov decision process (MDP) solution to optimize the timely-throughput objective subject to an average resource consumption constraint. Based on a packet-level decomposition of the MDP, we explicitly characterize the optimal scheduling policy and rigorously quantify the timely-throughput improvement due to predictive-service, which scales as $\Theta(p\left[C_{1}\frac{(a-a_{\max}q)}{p-q}\rho^{\tau}+C_{2}(1-\frac{1}{p})\right](1-\rho^{D}))$, where $a, a_{\max}, \rho\in(0, 1), C_1>0, C_2\ge0$ are constants, $p$ is the true-positive rate in prediction, $q$ is the false-negative rate, $\tau$ is the packet deadline and $D$ is the prediction window size. We also conduct extensive simulations to validate our theoretical findings. Our results provide novel insights into how prediction and system parameters impact performance and provide useful guidelines for designing predictive low-latency control algorithms.Comment: 14 pages, 7 figure

Transport mechanism for wireless micro sensor network

Wireless sensor network (WSN) is a wireless ad hoc network that consists of very large number of tiny sensor nodes communicating with each other with limited power and memory constrain. WSN demands real-time routing which requires messages to be delivered within their end-to-end deadlines (packet lifetime). This report proposes a novel real-time with load distribution (RTLD) routing protocol that provides real time data transfer and efficient distributed energy usage in WSN. The RTLD routing protocol ensures high packet throughput with minimized packet overhead and prolongs the lifetime of WSN. The routing depends on optimal forwarding (OF) decision that takes into account of the link quality, packet delay time and the remaining power of next hop sensor nodes. RTLD routing protocol possesses built-in security measure. The random selection of next hop node using location aided routing and multi-path forwarding contributes to built-in security measure. RTLD routing protocol in WSN has been successfully studied and verified through simulation and real test bed implementation. The performance of RTLD routing in WSN has been compared with the baseline real-time routing protocol. The simulation results show that RTLD experiences less than 150 ms packet delay to forward a packet through 10 hops. It increases the delivery ratio up to 7 % and decreases power consumption down to 15% in unicast forwarding when compared to the baseline routing protocol. However, multi-path forwarding in RTLD increases the delivery ratio up to 20%. In addition, RTLD routing spreads out and balances the forwarding load among sensor nodes towards the destination and thus prolongs the lifetime of WSN by 16% compared to the baseline protocol. The real test bed experiences only slight differences of about 7.5% lower delivery ratio compared to the simulation. The test bed confirms that RTLD routing protocol can be used in many WSN applications including disasters fighting, forest fire detection and volcanic eruption detection