Abstract-The spectrum usage by a secondary user often happens in a certain geographical region and in a certain time interval, and the requests often come in an online fashion. Considering the selfish behaviors of primary users and secondary users, it is imperative to design online double spectrum auction methods. The most significant challenge is how to make the online double auction economic-robust (truthful in particular). Unfortunately, existing designs either do not consider the online requests or become untruthful when applied to scenarios when both primary users and secondary users could be selfish.In this paper, we address this problem by proposing TODA, a general framework for truthful online double auction for spectrum allocation. We assume that there is a central auctioneer, and the arrivals of secondary users' requests follow Poisson distribution. Upon receiving online spectrum requests, the central auctioneer will decide immediately which secondary and primary users will win the auction, and match winning primary users and secondary users, as well as decide how much secondary users should pay and primary users should get. To preempt existing spectrum usage is not allowed. We study the case in which the conflict graph of secondary users is a complete graph, which occurs in the urban area where the distribution of the secondary users is very dense. In this case, we design strategyproof (truthful) mechanisms for both the primary users and secondary users. To the best of our knowledge, we are the first to design truthful online double auction mechanisms for spectrum allocation. Our simulation results show that the expected social efficiency ratio of our mechanism is always above 80% compared with the off-line VCG mechanism and the spectrum utilization ratio is around 70% when the system is highly loaded.

Data aggregation is an efficient primitive in wireless sensor network (WSN) applications. This paper focuses on data aggregation scheduling problem to minimize the latency. We propose an efficient distributed method that produces a collision-free schedule for data aggregation in WSNs. We prove that the latency of the aggregation schedule generated by our algorithm is at most 16R+Δ−14 time-slots. Here R is the network radius and Δ is the maximum node degree in the communication graph of the original network. Our method significantly improves the previously known best data aggregation algorithm [3], that has a latency bound of 24D + 6Δ + 16 time-slots, where D is the network diameter (Note that D can be as large as 2R). We conduct extensive simulations to study the practical performances of our proposed data aggregation method. Our simulation results corroborate our theoretical results and show that our algorithms perform better in practice.We prove that the overall lower-bound of latency of data aggregation under any interference model is max{log n, R} where n is the network size. We provide an example to show that the lowerbound is (approximately) tight under protocol interference model when r I = r, where rI is the interference range and r is the transmission range. We also derive the lower-bound of latency under protocol interference model when r < rI < 3r and rI ≥ 3r.

Abstract-In wireless sensor and actor networks (WSANs), a set of static sensor nodes and a set of (mobile) actor nodes form a network that performs distributed sensing and actuation tasks. In [1], Abbasi et al. presented DARA, a Distributed Actor Recovery Algorithm, which restores the connectivity of the inter-actor network by efficiently relocating some mobile actors when failure of an actor happens. To restore 1-and 2-connectivity requirements, two algorithms are developed in [1]. Their basic idea is to find the smallest set of actors that needs to be repositioned to restore the required level of connectivity, with the objective to minimize the movement overhead of relocation. Here, we show that the algorithms proposed in [1] will not work smoothly in all scenarios and we give counterexamples for some algorithms and theorems proposed in [1]. We then present a general actor relocation problem and propose methods that will work correctly for several subsets of the problems. Specifically, our method does result in an optimum movement strategy with minimum movement overhead for the problems studied in [1].

Data aggregation is a primitive communication task in wireless sensor networks (WSNs). In this paper, we study designing data aggregation schedules under the Protocol Interference Model for answering queries. Given a network consisting of a set of nodes V distributed in a two-dimensional plane, we address different kinds of queries in this paper. First and foremost, we consider a single one-off query which requires a subset of source nodes V ⊆ V to send data to a distinguished sink node, we propose a delay-efficient algorithm that produces a collision-free schedule and theoretically prove that the delay achieved by our algorithm is nearly a small constant factor of the optimum. We further extend our discussion to the multiple oneoff queries case and periodic query case and propose our data aggregation scheduling algorithms respectively with theoretical performance analysis.

Abstract-It is imperative to design efficient and effective online spectrum allocation methods since requests for spectrums often come in an online fashion. In this paper, we propose SALSA, strategyproof online spectrum admission for wireless networks. We assume that the requests arrival follows the Poisson distribution. Upon receiving an online spectrum request, our protocol will decide immediately whether to grant its exclusive usage or not, and how much the request should pay. Preempting existing spectrum usage is not allowed. We proposed two protocols that have guaranteed performances for two different scenarios: 1) random-arrival case: the bid values and requested time durations follow some distributions that can be learned, or 2) semi-arbitrary-arrival case: the bid values could be arbitrary, but the request arrival sequence is random. We analytically prove that our protocols are strategyproof, and are both approximately social efficient and revenue efficient. Our extensive simulation results show that they perform almost optimum. Our method for semi-arbitrary-arrival model achieves social efficiency and revenue efficiency almost 20-30 percent of the optimum, while it has been proven that no mechanism can achieve social efficiency ratio better than 1=e ' 37 percent. Our protocol for the randomarrival case even achieves social efficiency and revenue efficiency two-six times the expected performances by the celebrated VCG mechanism.