Robots are widely used for target search in applications such as search and rescue, environmental monitoring, and surveillance. Existing search algorithms typically rely on target signals or predictable movement patterns, which might not be available in real applications. In this paper, we address the practical challenge of a target search problem where targets do not emit signals and have unpredictable movement patterns. The problem is formulated as an area coverage problem: how to maximize the coverage of the robots’ detection areas within a limited time. We propose an algorithm that divides the search area into multiple partitions and assigns specific partitions to robots for maximizing their coverage and the success rate of target detection. Within each partition, the random walk technique is adopted by the robots to handle robot failures and unknown obstacles. Through theoretical analysis and experiments, we explore the optimal number of partitions as well as the optimal partition shape to facilitate searching. Extensive simulations across different dynamic environments validate the effectiveness and adaptability of our proposed algorithm.

In cognitive radio networks, rendezvous is a fundamental operation by which cognitive users establish communication links. Most of existing works were devoted to shortening the time-to-rendezvous (TTR) but paid little attention to qualities of the channels on which rendezvous is achieved. In fact, qualities of channels, such as resistance to primary users' activities, have a great effect on the rendezvous operation. If users achieve a rendezvous on a low-quality channel, the communication link is unstable and the communication performance is poor. In this case, re- rendezvous is required which results in considerable communication overhead and a large latency. In this paper, we first show that actual TTRs of existing rendezvous solutions increase by 65.40-104.38% if qualities of channels are not perfect. Then we propose a Quality-Aware Rendezvous Framework (QARF) that can be applied to any existing ren-dezvous algorithms to achieve rendezvous on high-quality channels. The basic idea of QARF is to expand the set of available channels by selectively duplicating high-quality channels. We prove that QARF can reduce the expected TTR of any rendezvous algorithm when the expanded ratio$\lambda$is smaller than the threshold$(-3+\sqrt{1+4(\frac{\sigma}{\mu})^{2}}) / 2$, where$\mu$and$\sigma$, respectively, are the mean and the standard deviation of qualities of channels. We further prove that QARF can always reduce the expected TTR of Random algorithm by a factor of$1+(\frac{\sigma}{\mu})^{2}$. Extensive experiments are conducted and the results show that QARF can significantly reduce the TTRs of the existing rendezvous algorithms by 10.50-51.05 % when qualities of channels are taken into account.

Fingerprint is one of the representative methods for wireless indoor localization. It uses a fingerprint database (measured in the offline phase) and the current received signal strengths (RSSs) (measured by the user's device in the online phase) to determine the location of this device. However, the RSSs and hence the localization accuracy would be affected by time-varying environmental factors (e.g., number of people in a shopping mall). In this paper, we propose a new method for wireless localization in time-varying indoor environments. In the offline phase, the proposed method measures extra information: it measures E fingerprint databases for E respective environmental conditions, where E is a design parameter (e.g., E=2 for the peak period and the non-peak period in a shopping mall). In the online phase, it leverages the extra information for better localization in time-varying indoor environment, even when the current environmental condition is different from the ones considered in the offline phase. The proposed method is particularly suitable for the indoor venues for which their primary concern is to provide good quality localization services while they could afford a moderate amount of extra resources for one-off measurement in the offline phase (e.g., exhibition centers, airports, shopping malls, etc.). We conduct a simulation experiment and a real-world experiment to demonstrate that the proposed method gives accurate localization.

Calibrators and sniffers have been used in the literature to proactively update the functional relationship between the received signal strength and the distance for wireless localization in time-varying indoor venue, where calibrators and sniffers are Wi-Fi transmitters and Wi-Fi receivers respectively. To be effective, these devices should be suitably placed in the indoor venue. Let there be N calibrators and M sniffers, di,jbe the distance between calibrator i and sniffer j, and RSSi,jbe the received signal strength measured by sniffer j from calibrator i. The points (d1,1, RSS1,1), (d1,2, RSS1,2), ..., (dN,M, RSSN,M) are used to estimate the functional relationship between the received signal strength and the distance. It is desirable that d1,1, d1,2, ..., dN,Mare uniformly scattered so that the estimated functional relationship is more accurate for better localization. In this paper, we propose to minimize the discrepancy of d1,1, d1,2, ..., dN,Min order to determine the optimal locations of the N calibrators and the M sniffers. We formulate this new problem (named minimal discrepancy placement problem) and design an efficient optimization method to solve it. We conduct simulation and real-world experiments to demonstrate that minimal discrepancy placement can effectively improve localization accuracy.

In cognitive radio networks, rendezvous is a fundamental operation by which cognitive users establish a communication link on a commonly-available channel for communications. Most of existing rendezvous algorithms provide guaranteed rendezvous (i.e., rendezvous can be achieved within finite time) by generating channel-hopping (CH) sequences based on the whole channel set. These algorithms are inefficient when available channels account for a small proportion of the whole channel set. Some recent algorithms generate CH sequences based on the available channel set. However, these algorithms normally require additional information such as unique IDs and predefined roles of cognitive users. In this study, we design a new algorithm called ZOS based on the set of available channels without any additional requirements. ZOS uses three types of elementary sequences (namely, Zero-type, One-type, and S-type) to generate CH sequences and provides guaranteed rendezvous. The maximum time-to-rendezvous of ZOS is upper-bounded by O(m1×m2×log2M) where M is the number of all channels and m1 and m2 are the numbers of available channels of two users. Simulation results show superior performance of ZOS.

In cognitive radio networks, rendezvous is a fundamental operation by which cognitive users establish communication links. Most of existing works consider rendezvous of a pair of users. When multiple pairs of users are doing rendezvous, collisions are caused by the multiple user-pairs which significantly degrade the rendezvous performance, e.g., resulting in a long time-to-rendezvous. To address this problem, we propose a new protocol called Cooperative Rendezvous Protocol which exploits cooperation in the multiple user-pairs environment to speed up the rendezvous operation. Using this protocol, multiple user-pairs cooperate with each other to relay their channel availability information, so that they could avoid attempting rendezvous in unavailable channels. The proposed protocol serves as a general framework that can be applied in conjunction with any existing rendezvous algorithm for faster rendezvous. We theoretically derive an upper bound on the time-to-rendezvous when the proposed protocol is applied in conjunction with any rendezvous algorithm which generates channel hopping sequence based on the available channel set. In addition, we conduct extensive simulation and the results show that the proposed protocol can significantly reduce the time-to-rendezvous of the existing rendezvous algorithms by up to 80.86% in multiple user-pairs environment.

Rendezvous is a fundamental operation for cognitive users to establish communication links so as to realize data communications and network management. Most of existing rendezvous algorithms implicitly assume that each cognitive user is equipped with one radio, i.e., one wireless transceiver. As the cost of wireless transceivers is dropping, it becomes economically feasible to utilize multiple radios to significantly improve the rendezvous performance. In this paper, we propose an Adjustable Multi-Radio Rendezvous (AMRR) algorithm which exploits multiple radios for fast rendezvous based on available channels only. Suppose that a cognitive user is equipped with m radios. Our basic idea is to partition the radios into two groups: k stay radios and (m - k) hopping radios. The user stays on specific channels in the stay radios while hops on its available channels parallelly in the hopping radios. We prove that the maximum time-to-rendezvous (MTTR) of AMRR is upper-bounded by O(|C1||C2|/m1m2), where |C1| and |C2| are the numbers of available channels of two users and m1and m2are the numbers of radios of the two users. This bound meets the lower bound of MTTR of any deterministic rendezvous algorithm when two users are equipped with the same number of radios (i.e., m1= m2). AMRR is adjustable in giving its best performance on either MTTR or E(TTR) by adjusting value of k. Simulation results show that AMRR performs better than the state-of-the-art.

Rendezvous is a fundamental operation in cognitive radio networks (CRNs) for establishing a communication link on a commonly-available channel between cognitive users. The existing work on rendezvous implicitly assumes that each cognitive user is equipped with one radio (i.e., one wireless transceiver). As the cost of wireless transceivers is dropping, this feature can be exploited to significantly improve the rendezvous performance at low cost. In this study, we investigate the rendezvous problem in CRNs where cognitive users are equipped with multiple radios and different users may have different numbers of radios. We first study how the existing rendezvous algorithms can be generalized to use multiple radios for faster rendezvous. We then propose a new rendezvous algorithm, called role-based parallel sequence (RPS), which specifically exploits multiple radios for more efficient rendezvous. Our basic idea is to let the cognitive users stay in a specific channel in one dedicated radio and hop on the available channels with parallel sequences in the remaining general radios. We prove that our algorithm provides guaranteed rendezvous (i.e., rendezvous can be completed within a finite time) and derive the upper bounds on the maximum time-to-rendezvous (TTR) and the expected TTR. The simulation results show that i) multiple radios can cost-effectively improve the rendezvous performance, and ii) the proposed RPS algorithm performs better than the ones generalized from the existing algorithms.

Rendezvous is a necessary operation for cognitive users to establish communication links in cognitive radio networks (CRNs). To guarantee the rendezvous in finite time, all existing rendezvous algorithms generate CH (channel-hopping) sequences using the whole channel set and attempt rendezvous on each of the channels (i.e., both available channels and unavailable channels). In practice, the available channel set is usually a small portion of the whole channel set due to dynamics of channel availabilities and limited sensing capabilities of cognitive users. Thus, the CH sequences using the whole channel set may attempt unnecessary rendezvous in uncertain channels (e.g., unavailable channels or randomly-selected channels) which greatly degrades the performance. In this study, we propose a new rendezvous algorithm that generates channel-hopping sequences based on available channel set (CSAC) for more efficient rendezvous. We prove that CSAC gives guaranteed rendezvous and derive its upper-bound on maximum time-to-rendezvous (MTTR) which is an expression of the number of available channels instead of the number of all potential channels. To the best of our knowledge, CSAC is the first one in the literature that exploits the only available channels in designing CH sequences while providing guaranteed rendezvous. Experimental results show that CSAC can significantly improve the MTTR compared to state-of-the-art.

Rendezvous is a fundamental operation in cognitive radio networks (CRNs) for establishing a communication link on a commonly-available channel between cognitive users. The existing works on rendezvous implicitly assume that each cognitive user is equipped with one radio (i.e., one wireless transceiver). As the cost of wireless transceivers is dropping, this feature can be exploited to significantly improve the rendezvous performance at low cost. In this study, we investigate the rendezvous problem in CRNs where cognitive users are equipped with multiple radios and different users may have different number of radios. We first study how the existing rendezvous algorithms can be generalized to use multiple radios for faster rendezvous. We then propose a new rendezvous algorithm, called role-based parallel sequence (RPS), which specifically exploits multiple radios for more efficient rendezvous. Our basic idea is to let the cognitive users stay in a specific channel in one dedicated radio and hop on the available channels with parallel sequences in the remaining general radios. We prove that our algorithm provides guaranteed rendezvous and derive the maximum time-to-rendezvous (TTR) and upper-bounds on the expected TTR. Extensive experiments are conducted to evaluate the proposed solutions.