The presence of unauthorized radio frequency interference (RFI) sources seriously affects the retrieval of brightness temperature of a synthetic aperture interferometric radiometer (SAIR). Postobservation RFI geolocation is important for improving the RFI situation hereafter and mitigating the impact of the identified RFIs. Previous works have demonstrated the potential of using high-/super-resolution direction-of-arrival (DOA) estimation techniques to achieve accurate localization of RFI sources in SAIR imagery. In this article, a multiple snapshot difference method is proposed to further reduce the RFI source geolocation bias. The covariance matrices of the consecutive snapshots with power changed RFIs are picked out and subtracted to cancel out the background scene. A MUSIC-based DOA algorithm is developed. Numerical simulations validate the theoretical correctness and the practical effectiveness of the proposed method.

Let S⊆V(G) and κG(S) denote the maximum number r of edge-disjoint trees T1,T2,…,Tr in G such that V(Ti)∩V(Tj)=S for any i,j∈{1,2,…,r} and i≠j⁠. For an integer k with 2≤k≤n⁠, the generalized k-connectivity of a graph G is defined as κk(G)=min{κG(S)|S⊆V(G) and |S|=k}⁠. The generalized k-connectivity is a generalization of the traditional connectivity. In this paper, the generalized 3-connectivity of the (n,k)-bubble-sort graph Bn,k is studied for 2≤k≤n−1⁠. We show that κ3(Bn,k)=n−2 for 2≤k≤n−1⁠, which generalizes the known result about the bubble-sort graph Bn (Li, S., Tu, J. and Yu, C. (2016) The generalized 3-connectivity of star graphs and bubble-sort graphs. Appl. Math. Comput., 274, 41–46), as the bubble-sort graph Bn is the special (n,k)-bubble-sort graph for k=n−1⁠.

In this paper, we aim to maximize users' satisfaction by deploying limited number of relays in a target region to form a wireless relay network, and define the Deployment of Cooperative Relay (DoCR) problem, which is proved to be NP-complete. We first propose an O(δ log n) approximation algorithm that utilizes the algorithms for budget weighted Steiner tree problem with novel position weighting assignment. We further propose a heuristic method to solve the DoCR problem releasing potential location constraint. Our extensive experiments indicate that the algorithms we propose can significantly improve the total satisfaction of the network. Furthermore, we establish a testbed using USRP to showcase our designs in real scenarios. To the best of our knowledge, we are the first to propose approximation algorithm for relay placement problem to maximize user satisfaction, which has both theoretical and practical significance in the related area.

One type of distributed systems is the client/server system consist of clients and servers. In order to improve the performance of such a system, client assignment strategy plays an important role. There are two criteria to evaluate the load on the servers - total load and load balance. The total load increases when the load balance decreases, vice versa. It has been proved that finding the best client assignment is NP-hard. In this paper, we propose a new model for the client assignment problem and design an algorithm based on Semidefinite programming (SDP). Our method has a (relaxed) performance ratio 0.87 when only 2 servers exist. In general case, our method becomes a heuristic, and the ratio of each iteration is 0.87. We are the first one to give these bounds. Our simulation results are compared with the state-of-art client assignment method, and our strategy outperforms it in terms of running time while keeps the load in similar level.

In wireless sensor network, each sensor can monitor an area, which is a disk with center at the sensor. Considering a set of sensors and given a target area, how do we select a subset of sensors to monitor the target area?Note that a whole area is monitored (or say covered) if every point in the area is covered. We cannot check at every point. Usually, one selects a set of points in the target area, called target points, such that the target area is covered by a subset of sensors if and only if all target points are covered.The question is how many target points can replace a target area in such a way? The existing method needs O(n2) target points when n sensors are considered. In this paper, we propose a method to reduce this number. This would improve the performance of wireless sensor networks.

Given a requested area, the Minimum Connected Sensor Cover problem is to find a minimum number of sensors such that their communication ranges induce a connected graph and their sensing ranges cover the requested area. Several polynomial-time approximation algorithms have been designed previously in the literature. Their best known performance ratio is O(r ln n) where r is the link radius of the sensor network and n is the number of sensors. In this paper, we will present two polynomial-time approximation algorithms. The first one is a random algorithm, with probability 1 - ε, producing an approximation solution with performance ratio O(log3n log log n), independent from r. The second one is a deterministic approximation with performance ratio O(r), independent from n.

In this paper we present a middleware named SmartPrint to provide cloud print service in office, where many heterogeneous networks exist. The goal of the system is to shield the communication heterogeneity of the devices in the office and make authorized users freely connect to all the printers with no modification on their terminals. SmartPrint can manages all the printers in an office building, and it provides friendly service for the users who know nothing about the printers. SmartPrint can also automatically choose printers for the office staffs. We propose and implement two printer allocation methods, one aims to improve the experience of the user with short print job, and the other is a multiple attributes decision algorithm which considers all factors including spatial information that impact the user experiences. Through experiments we validate the methods, and prove that SmartPrint achieves high user satisfaction from collected real data.

When a large amount of sensors are randomly deployed into a field, how can we make a sleep/activate schedule for sensors to maximize the lifetime of target coverage in the field? This is a well-known problem, called Maximum Lifetime Coverage Problem (MLCP), which has been studied extensively in the literature. It is a long-standing open problem whether MLCP has a polynomial-time constant-approximation. The best-known approximation algorithm has performance ratio 1 + ln n where n is the number of sensors in the network, which was given by Berman et. al [1]. In their work, MLCP is reduced to Minimum Weight Sensor Coverage Problem (MWSCP) which is to find the minimum total weight of sensors to cover a given area or a given set of targets with a given set of weighted sensors. In this paper, we present a polynomial-time (4 + ∈)-approximation algorithm for MWSCP and hence we obtain a polynomial-time (4 + ξ)-approximation algorithm for MLCP, where ∈ >; 0, ξ >; 0.