In the context of Internet of Things (IoT) networks, this work investigates an active reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system and establishes an integrated analytical frame-work that rigorously characterizes both the inherent double-fading effect of RIS-assisted propagation and the power-gain coupling introduced by the active RIS. A weighted sum-rate (WSR) maximization problem is then formulated under the constraints of access point (AP) power budgets, the RIS power consumption limitations, and the receiver energy harvesting requirements. To manage the resulting highly non-convex structure, the original problem is equivalently reformulated as a weighted minimum mean squared error (WMMSE) minimization problem, which enables the development of an efficient alternating optimization algorithm. At each iteration, the MMSE decoding vectors are firstly updated in closed form, while the energy signal covariance matrices, the AP information beamformers, and the active RIS reflection coefficients are subsequently optimized via semi-definite programming, successive convex approximation, and augmented Lagrangian method, respectively. Extensive simulation results demonstrate that the adoption of an active RIS effectively mitigates the double-fading effect and achieves performance comparable to that of passive RIS with significantly fewer reflecting elements, while the proposed joint optimization strategy yields substantial WSR gains and further enlarges the performance gap between the optimized active and passive RIS schemes.

Many complex networks can be modeled as evolving graphs. Existing in-memory graph data structures are designed for DRAM. Thus, they cannot effectively exploit the current and ongoing adoption of emerging non-volatile main memory (NVMM) for two reasons. (1) Ephemeral graph data structures are not crash-consistent for NVMM. (2) NVMM writes and reads and may incur higher latency than DRAM. In this article, we propose a novel persistent evolving graph data structure, named NVGRAPH, for both computing and in-memory storage of evolving graphs in NVMM. We devise NVGRAPHas a multi-version data structure, wherein a minimum of one version of its data is stored in NVMM to provide the desired durability at runtime for failure recovery, and another version is stored in both DRAM and NVMM to reduce the NVMM-induced memory latency. NVGRAPHis also a partitioned data structure. We dynamically transform the layout of NVGRAPH(e.g., changing the size of its partition in DRAM) exploiting network properties and data access patterns of workloads. For the evaluation of NVGRAPH, we implement four representative real-world graph applications: pagerank, BFS, influence maximization, and rumor source detection. The experimental results show that the performance of NVGRAPHis comparable to other in-memory data structure (e.g., CSR and LLAMA) while using 70 percent less DRAM. It scales well up to 10 billion edges and 201 snapshots and supports crash consistency. It offers up to the 21X speedup of execution time compared to the scale-up graph computation approaches (e.g., GraphChi and X-stream).

Evolving graph processing has enabled the modeling of many complex network systems, e.g., online social networks and gene networks. Existing in-memory graph data structures cannot effectively exploit the current and ongoing adoption of emerging non-volatile main memory (NVMM) for two reasons. (1) Ephemeral graph data structures are not crash-consistent nor durable for NVMM. Corruption is likely when updating its correlated application-defined data and runtime states in the face of hardware or software failures. (2) NVMM writes and reads may incur higher latency than DRAM. Placing the data structures in NVMM may result in a significant loss of performance. In this paper, we propose a novel persistent evolving graph data structure, named NVGRAPH, for both computing and in-memory storage of evolving graphs in NVMM. We devise NVGRAPH as a multi-version data structure, wherein a minimum of one version of its data is stored in NVMM to provide the desired durability at runtime for failure recovery, and another version is stored in both DRAM and NVMM to reduce the NVMM-induced memory latency. We dynamically transform the layout of NVGRAPH including changing the size of its partition in DRAM and the position of its base snapshot exploiting network properties and data access patterns of workloads. For the evaluation of NVGRAPH, we implement four representative real-world graph applications: pagerank, BFS, influence maximization, and rumor source detection. The experimental results show that the performance of NVGRAPH is comparable to other in-memory data structure (e.g., CSR and LLAMA) while using 70% less DRAM. It scales well up to 10 billion edges and 201 snapshots and supports crash consistency. It offers up to the 21X speedup of execution time compared to the scale-up graph computation approaches (e.g., GraphChi and X-stream).

Online Social networks (OSNs) are now one of the main resources for people to keep abreast of current news and to exchange opinions about new products and social trends, etc. However, unethical use of OSNs also provides a convenient conduit to the diffusion of malicious rumors and misinformation, thus it is of significant importance to discover rumor diffusion and detect the rumor source. This is a very challenging task, as shown in many existing works, e.g., even in the regular tree graphs, the accuracy of detecting the information source from a diffusion snapshot cannot exceed 31%. To overcome this issue, in this work, we propose a novel system framework for information source detection in OSNs and investigate a new rumor source detection problem, called k-Minimum Distance Rumor Source Detection (k-MDRSD). Specifically, given a rumor spreading snapshot, our target is to find a small set of rumor candidates which can be used as initial seeds for further iterative query or investigation. To this end, we introduce a notion, called distance error, for rumor candidate sets and formulate the k-MDRSD problem. Resorting to methods from Combinatorics, we develop a near optimal algorithm for k-MDRSD. By experimental simulation, we show that the proposed k-MDRSD significantly improves the likelihood of detecting rumor sources or trend-setters in OSNs.

Spectrum trading benefits secondary users (SUs) with accessing opportunities and primary users (PUs) with monetary gains in cognitive radio networks. In the spectrum trading market, bidding privacy is a serious concern for SUs, since untrustworthy PUs or spectrum traders may leverage bidding information to manipulate the trading or leak SU's bids to other SU competitors for illegal gains. Current centralized privacy preserving spectrum trading designs may incur extra infrastructure deployment, miss many instantaneous spectrum accessing opportunities, and have scalability issues. In this paper, we propose a novel privacy-preserving semi-distributed spectrum trading scheme, which has joint consideration of spectrum reuse, SUs' bidding privacy preservation, and PUs' revenue maximization. We use the conflict graph to characterize SUs' interference relationships, and jointly employ the Paillier Cryptosystem and dynamic matching with evolving preferences to conduct the privacy-preserving spectrum trading in a semi-distributed manner. Through security, complexity and performance analysis, we show that the proposed scheme can effectively preserve the privacy of SUs' bidding values, and notably increase the PUs' revenue and improve spectrum utilization with limited overhead.

On-demand wireless data broadcast is an efficient way to disseminate data to a large number of mobile users. In many applications, such as stock quotes and flight schedules, users may have to download multiple data items per request. However the multi-item request scheduling has not yet been thoroughly investigated for on-demand wireless data broadcasts. In this paper, we step-up on investigating this problem from viewpoint of theory and simulation. We develop a two-stage scheduling scheme to arrange the requested data items with the objective of minimizing the average access latency. The first stage is to select the data items to be broadcast in the next time period and the second stage is to schedule the broadcasting order for the data items selected in the first stage. We develop algorithms for the two stages respectively and analyze them both theoretically and practically. We also compare the proposed algorithms with other well known scheduling methods through simulation. The theoretical findings and simulation results reveal that significantly better access latency can be obtained by using our scheduling scheme rather than its competitors.

Energy efficiency is an important issue in the study of wireless sensor networks. Given a set of targets and a set of sensors with bounded lifetime, the maximum lifetime k-coverage problem is to schedule active/sleeping status of sensors to maximize the time period during which every target is covered by at least k active sensors. Previously, it was known that when the sensing ranges are uniform, this problem has a polynomial time (4+ε)-approximation for k = 1 and (6+ε)-approximation for k = 2. In this paper, we make significant progress by showing that for any positive integer k, there exists a polynomial-time (3 + ε)-approximation.

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.

Wireless data broadcast is an efficient way to disseminate data to a large number of users in mobile communication environments. In many applications, such as stock quotes, flight schedules and traffic news, the users may want to download multiple data items at one time and the applications may require the support of multi-channel architectures. This paper studies the problem of retrieving a set of data items from multiple wireless broadcasting channels such that all the requested data items can be downloaded with the least access time, namely Least Time Data Retrieval (LTDR). On one hand, we find that it is NP-hard to approximate this problem to within any non-trivial factor even if there are only two channels. On the other hand, we find that there exists a polynomial time (1+1/1-α)-approximation solution for an LTDR instance L if the optimal solution of L requirers τ time slots and (1-α)kT ≤ τ ≤ kT, where k is a positive integer, α is an arbitrary constant between 0 and 1, and T is the least common multiple of cycle lengths for all channels. We also derive a lower bound of polynomial time approximation for L matching that upper bound under the well known assumption P ≠ NP. To improve the practical efficiency of the approximation solution, a heuristic is exhibited and tested by simulations.

Due to technological advances in micro-electronics, digital electronics, and wireless data communications, we have witnessed the advent of Wireless Sensor Networks (WSNs) and related applications in many areas such as battlefield surveillance, environmental monitoring, and biomedical observation. In this paper, we investigate the Maximum Directional Target Coverage Problem (MDTCP) for a special group of wireless sensor networks in which each sensor has a specific coverage range and a limited coverage angle. Given a set of sensors and a set of target points in a finite area, the objective of MDTCP is to cover the maximum number of target points by adjusting the directions of sensors. We develop a polynomial time approximation algorithm with performance ratio 1-e/e for MDTCP. In addition, we also analyze the structure of MDTCP from the Combinatorics' point of view.

Wireless data broadcast is a popular data dissemination method in mobile computing environments because of its capability of concurrently disseminating data to multiple users. In this paper, we study the data retrieval scheduling problem for multi-item requests in multi-channel broadcast environments. To maximize the number of downloads given a deadline, we define a problem called largest number data retrieval (LNDR). We prove the decision problem of LNDR is NP-hard, and we investigate approximation algorithm for it. We also define another problem called minimum cost data retrieval (MCDR), which aims at downloading a set of requested data items with the least response time and energy consumption. We prove MCDR is NP-hard to approximate to within any non-trivial factor. Therefore, we investigate heuristic algorithm for it. Finally we provide simulation results to demonstrate the practical efficiency of the proposed algorithms.

In many real-world scenarios, social network serves as a platform for information diffusion, alongside with positive information (truth) dissemination, negative information (rumor) also spread among the public. To make the social network as a reliable medium, it is necessary to have strategies to control rumor diffusion. In this article, we address the Least Cost Rumor Blocking (LCRB) problem where rumors originate from a community Cr in the network and a notion of protectors are used to limit the bad influence of rumors. The problem can be summarized as identifying a minimal subset of individuals as initial protectors to minimize the number of people infected in neighbor communities of Cr at the end of both diffusion processes. Observing the community structure property, we pay attention to a kind of vertex set, called bridge end set, in which each node has at least one direct in-neighbor in Cr and is reachable from rumors. Under the OOAO model, we study LCRB-P problem, in which α (0 < 1) fraction of bridge ends are required to be protected. We prove that the objective function of this problem is submodular and a greedy algorithm is adopted to derive a (1 -- 1/e)-approximation. Furthermore, we study LCRB-D problem over the DOAA model, in which all the bridge ends are required to be protected, we prove that there is no polynomial time o(ln n)-approximation for the LCRB-D problem unless P = NP, and propose a Set Cover Based Greedy (SCBG) algorithm which achieves a O(ln n)-approximation ratio. Finally, to evaluate the efficiency and effectiveness of our algorithm, we conduct extensive comparison simulations in three real-world datasets, and the results show that our algorithm outperforms other heuristics.

Influence maximization problem is to find a set of seeds in social networks such that the cascade influence is maximized. Traditional models assume all nodes are willing to spread the influence once they are influenced, and they ignore the disparity between influence and profit of a product. In this paper by considering the role that price plays in viral marketing, we propose price related (PR) frame that contains PR-I and PR-L models for classic IC and LT models respectively, which is a pioneer work. We find that influence and profit are like two sides of the coin, high price hinders the influence propagation and to enlarge the influence some sacrifice on profit is inevitable. We propose Balanced Influence and Profit (BIP) maximization problem. We prove the NP-hardness of BIP maximization under PR-I and PR-L model. Unlike influence maximization, the BIP objective function is not monotone. Despite the non-monotony, we show BIP objective function is sub modular under certain conditions. Two unbudgeted greedy algorithms separately are devised. We conduct simulations on real-world datasets and evaluate the superiority of our algorithms over existing ones.

Wireless data broadcast is an efficient way of disseminating data to users in the mobile computing environments. From the server's point of view, how to place the data items on channels is a crucial issue, with the objective of minimizing the average access time and tuning time. Similarly, how to schedule the data retrieval process for a given request at the client side such that all the requested items can be downloaded in a short time is also an important problem. In this paper, we investigate the multi-item data retrieval scheduling in the push-based multichannel broadcast environments. We prove the decision version of this problem is NP-complete, and we devise an algebraic algorithm to search for the best solution. We also develop a heuristic that can employ the algebraic algorithm to download a large number of items efficiently. When there is no replicated item in a broadcast cycle, we show that an optimal retrieval schedule can be obtained in polynomial time. The performances of proposed algorithms are analyzed theoretically and evaluated through simulation. The experimental results show that our algorithms can significantly reduce the access time for multi-item requests.

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.

Wireless data broadcast is an efficient technique of disseminating data simultaneously to a large number of mobile clients. In many information services, the users may query multiple data items at a time. In this paper, we study the data retrieval scheduling problem from the client's point of view. We formally define the Largest Number Data Retrieval (LNDR) problem with the objective of downloading the largest number of requested data items in a given time duration, and the Minimum Cost Data Retrieval (MCDR) problem which aims at downloading a set of data items with the minimum energy consumption. When the time needed for channel switching can be ignored, a Maximum Matching optimal algorithm is exhibited for LNDR which requires only polynomial time; when the switching time cannot be neglected, LNDR is proven to be NP-hard and a greedy algorithm with constant approximation ratio is developed. We also prove that the MCDR problem is NP-hard to be approximated within to any nontrivial factor and a parameterized heuristic is devised to solve MCDR non-optimally.

BACKGROUND: Proteins show a great variety of 3D conformations, which can be used to infer their evolutionary relationship and to classify them into more general groups; therefore protein structure alignment algorithms are very helpful for protein biologists. However, an accurate alignment algorithm itself may be insufficient for effective discovering of structural relationships among tens of thousands of proteins. Due to the exponentially increasing amount of protein structural data, a fast and accurate structure alignment tool is necessary to access protein classification and protein similarity search; however, the complexity of current alignment algorithms are usually too high to make a fully alignment-based classification and search practical. RESULTS: We have developed an efficient protein pairwise alignment algorithm and applied it to our protein search tool, which aligns a query protein structure in the pairwise manner with all protein structures in the Protein Data Bank (PDB) to output similar protein structures. The algorithm can align hundreds of pairs of protein structures in one second. Given a protein structure, the tool efficiently discovers similar structures from tens of thousands of structures stored in the PDB always in 2 minutes in a single machine and 20 seconds in our cluster of 6 machines. The algorithm has been fully implemented and is accessible online at our webserver, which is supported by a cluster of computers. CONCLUSION: Our algorithm can work out hundreds of pairs of protein alignments in one second. Therefore, it is very suitable for protein search. Our experimental results show that it is more accurate than other well known protein search systems in finding proteins which are structurally similar at SCOP family and superfamily levels, and its speed is also competitive with those systems. In terms of the pairwise alignment performance, it is as good as some well known alignment algorithms.