This work proposes a socially-aware approach for efficient point cloud multicast and redirected walking in mobile social virtual reality (VR). The proposed approach tackles high bandwidth demand and dynamic user mobility in immersive VR applications by integrating tile-based point cloud transmission, point cloud registration, multicast with Device-to-Device (D2D) communication, and redirected walking. The objective is to reduce redundant transmission through point cloud registration, improve the quality of communication via motion-aware redirection, and ensure privacy protection. To achieve this, we formulate a new optimization problem and design an algorithm named D2D Multicast Tile Registration (DMTR). The algorithm evaluates the feasibility of D2D connections based on social ties and privacy protection, and classifies tiles to maximize registration efficiency. We compare our approach with state-of-the-art algorithms through comprehensive simulations, using total cost, D2D efficiency, and registration ratio as evaluation metrics. The simulation results demonstrate that DMTR outperforms the baselines by more than 20% in terms of total cost.

The rapid development of multi-view videos (MVV) transmission is an irresistible trend. Concurrently, reconfigurable intelligent surface (RIS)-assisted wireless communication has drawn significant attention. We observe that the view selection based on the base station and the view synthesis based on depth-image-based rendering (DIBR) can effectively reduce power consumption. Therefore, this paper studies the view selection and synthesis for RIS-aided MVV in multigroup multicast beamforming. To deal with this complicated scenario, we investigate a problem, named the joint View selection, Multicast beamforming, and DIBR (JVMD), to minimize the total multicast beamforming power, the view transmission operation power, and view synthesis, subject to quality-of-service (QoS), RIS phase shifts, view selection, and DIBR constraints. Unfortunately, the mathematical model is a complicated mixed discrete-continuous optimization problem. To tackle this challenging problem, we designed an algorithm, named View selection, Beamforming, RIS phase, and DIBR (VBRD) algorithm. First, we deal with the discrete optimization problem of selecting the view. VBRD uses the dual-based approximation methodology to round back a primal's integer solution. Then, in the continuous optimization problem, we apply the alternating optimization (AO) method to determine beamforming, RIS phase, and DIBR. Finally, simulation results show the performance of exploiting view synthesis for RIS-assisted wireless communication.

New radio (NR) and non-orthogonal multiple access (NOMA) offer scalable and efficient resource allocation in Beyond 5G (B5G) networks. NR implements mixed numerology with flexible frame structures for future compatibility, whereas NOMA allows users with different channel states to share an identical Physical Resource Block (PRB). Multi-connectivity enables a user to connect to multiple networks for reliability, and multicast conveys data to users simultaneously that request the same content. However, resource allocation in the NOMA-based mixed numerology system with multi-connectivity for multicast remains unexplored. The problem is challenging due to 1) the different shapes of PRBs in NR and 2) the shared locations of PRBs in a frame with NOMA. In this paper, we formulate a new optimization problem, named Multicast, Multi-connectivity, and Multi-Dimensional Resource Allocation Problem (M3DRAP), and prove its NP-hardness and inapproximability. We propose an approximation algorithm for general M3DRAP with the ideas ofMulticast Inter-Numerology Relation,Layer Dissimilarity,Subgrouping Nonuniformity, andSegmentation Preference. To find the intrinsic properties of PRB allocation for multicast in NOMA-based networks, we consider a single B5G usage scenario (e.g., eMBB, URLLC, or mMTC) and propose another approximation algorithm. Simulations demonstrate our algorithms improve the weighted sum rate by over 50% and increase the user satisfaction ratio by 1.5x.

With the emergence of new applications for holographic-type communication in healthcare, entertainment, and education, point cloud video transmission has become essen-tial. This paper aims to reduce the bandwidth cost by leveraging tile-based video transmission with point cloud registration in a wireless multicast network. A point cloud video is divided into multiple tiles, and each tile contains a portion of point cloud objects and can be registered by adjacent tiles with some similar objects under the registration rotation and registration overlap constraints. We formulate a new optimization problem and prove that it is NP-hard, and then we design an algorithm Multicast Multi-Tile Registration (MMTR) to select multicasting and registered tiles under consideration of socially related users' preferences with the idea of a tile registration graph. A more popular tile can be multicasted to more friends to minimize the bandwidth cost. Experimental results with real datasets show that MMTR can reduce bandwidth costs by more than 20% and achieve better video quality compared to state-of-the-art point cloud transmission algorithms.

Wireless power transfer (WPT) has emerged to enhance the robustness of the energy harvesting Internet of Things (EH-IoT), whereas beamforming has been leveraged to significantly boost the efficiency of far-field WPT. Nevertheless, potential negative impacts due to high electromagnetic fields (EMF) exposure for radiation-susceptible users have not been thoroughly considered in the design of WPT for EH-IoT with IoT application-level requirements (e.g., coverage). In this article, we explore the health-aware beamforming and IoT selection problem under the EH and human safety constraints. First, we formulate a new optimization problem Health-Aware Beamforming and IoT Selection (HABIS) and prove the NP-hardness. Second, we design an approximation algorithm, named Minimum Radiation Exposure and Maximum IoT Coverage (MREMIC), to exploit the EH-health dependency (EHHD) graph for properly addressing the trade-off between EH efficiency and potential EMF radiation exposure to human bodies. We also discover the optimal health-aware beamforming to minimize the total radiation energy absorption of humans. Simulation results show that MREMIC can effectively charge IoT devices and significantly outperforms existing EH approaches by more than 200% regarding human safety.

Multi-view videos (MVV) have emerged to provide users with immersively interactive experiences with 3D multimedia content. Compared with traditional 2D videos, MVV offers multiple view angles to avoid generating occluded regions from a single viewpoint and allows users to receive different view angles, which consume much higher bandwidth. In this paper, we leverage multicast and view synthesis to effectively reduce the number of transmitted views and total bandwidth consumption in software-defined content delivery networks (SD-CDN). By exploiting the SDN architecture, SD-CDN can optimize traffic engineering and the selection of multi-view sources to serve users. We formulate a new optimization problem, (), prove the NP-hardness, and design an online algorithm with the ideas of View Popularity Cost Ratio, View Watching Possibility, and synthesis tree, to achieve the tightest competitive ratio. The experiment on real networks and implementation in an experimental SDN manifest that the proposed algorithm outperforms state-of-the-art algorithms regarding the total cost, bandwidth consumption, synthesis quality, and link and node utilization.

The virtual reality (VR) market is expected to reach 202.7 billion dollars by 2028, at a compound annual growth rate of 24.74% over the forecast period 2023–2028. It motivates innovative VR services in touring, E-commerce, and social activities, and effective VR video streaming becomes essential. However, VR services are envisaged to consume a large amount of bandwidth, but current research primarily focuses on multimedia streaming for each individual user without considering the opportunity of view synthesis for multicast to reduce wireless resource consumption further. In this article, we formulate a new optimization problem VR Content Sharing and Multicasting (VCSM) and prove the NP-hardness. Then, we propose an approximation algorithm, named Efficient View Synthesis and Multicasting (EVSM), to select multicast views and their Modulation and Coding Schemes (MCS) for wireless VR services. Afterward, we extend EVSM to support dynamic user behaviors and increase scalability with distributed mobile edge computing. We also explore the intrinsic properties of view selections to find the optimal solution for regular user deployment. Experiment results show that EVSM can effectively reduce bandwidth consumption for VR services by more than 50$\%$.

The emergence of AI has shifted the focus of wireless communications towards deep semantic-level fidelity (i.e., semantic communication networks), emphasizing the significance and effectiveness of transmitted data. However, semantic feature selection considering multicast users with social relations for feature sharing has not been explored. In this paper, we formulate a new optimization problem to minimize the total communication, forwarding, and computation costs, with the proof of NP-hardness and inapproximability. We propose a new algorithm, Multicast Semantic Feature Selection (MSFS), with the notions of Cross Task Semantic Indicator, Substituted Subgraph, and Socially Feature Selection Indicator, to select features on different mobile edge computing servers and cluster the users to receive features via multicast. Simulations with real datasets manifest that MSFS can reduce the total cost by more than 50% compared with state-of-the-art algorithms.

The localization of Road Users (RUs) is an important issue in adverse event prevention due to the unreliable nature of GPS and the high cost of high-precision location acquisition sensors. In addition, previous research on adverse event prevention on roads has not taken into account RUs in blind spots at the same time. To address these issues, we investigate a collaboration system for heterogeneous RUs and Mobile Edge Computing (MEC) servers, called Collaborative Adverse Event Prevention system (CAEP) to efficiently alert RUs to potential adverse events and perceive the blind spot of the RUs. CAEP includes two AI-based functional modules, a localization module and a blind spot detection module, and an adverse event prevention algorithm. The localization module localizes each RU and the blind spot detection module detects the other RUs in the blind spot. The adverse event prevention algorithm jointly considers general road collision events and the event of a difference in radius between the inner wheels of a vehicle to completely include adverse events on the road. We implement CAEP in a real-world traffic environment with Jetson AGX Xavier devices and cameras to evaluate the performance. Our evaluation shows that CAEP provides RUs with sufficient preparation time to prevent adverse events and correctly detects the RUs in blind spots.

Previous research on SDN multicast traffic engineering mainly focused on intra-domain optimization. However, the main traffic on the Internet is inter-domain, and the selection of border nodes and sharing of network information between domains are usually distributed but ignored in previous works. In this article, we explore multi-domain online distributed multicast traffic engineering (MODMTE). To effectively solve MODMTE, we first prove that MODMTE is inapproximable within$|D_{\max }|$, which indicates that it is impossible to find any algorithm with a ratio better than$|D_{\max }|$for MODMTE, and$|D_{\max }|$is the maximum number of destinations for a multicast tree. Then, we design a$|D_{\max }|$-competitive distributed algorithm with the ideas of Domain Tree, Dual Candidate Forest Construction, and Forest Rerouting to achieve the tightest performance bound for MODMTE. Experiments on a real SDN with YouTube traffic manifest that the proposed distributed algorithm can reduce more than 30% of the total cost of bandwidth consumption and rule updates for multicast tree rerouting compared with the state-of-the-art algorithms.

3GPP has defined Dual Connectivity (DC) to allow a user to access a 4G and a 5G base station (BS) simultaneously. However, the resource allocation for DC is challenging because of not only the co-channel interference between 4G and 5G BSs but also different shapes of Resource Blocks (RBs) for New Radio (NR) and the reuse of RBs for Non-Orthogonal Multiple Access (NOMA). In this paper, we formulate Dual Connectivity Multidimensional Resource Allocation Problem and prove that it is NP-hard. We design an approximation algorithm with the ideas of 1) Zone Shaping, 2) Occupancy Indicator and Overlap Degree of RBs, 3) DC Slicing, and 4) DC Inter-Numerology Relation, to maximize the total throughput of heterogeneous user demands in the coexisting 4G and 5G network with NR, NOMA, and DC. Simulation results manifest that our algorithm outperforms the state-of-the-arts regarding throughput and resource efficiency.

With the advances in Social Internet of Things (SIoT) and Federated learning (FL), smart devices are now able to cooperatively and locally perform learning tasks to protect sensitive data by Differential Privacy (DP). On the other hand, Hierarchical FL (HFL) clusters SIoTs into multiple local training groups to reduce communication overheads by local aggregation. In this paper, we explore SIoT Training Group Construction (STGC) for HFL to minimize the total SIoT computation, communication and hiring costs, and the privacy cost for exploiting DP. We prove that STGC is NP-hard and inapproximable within any factor unless P = NP. Then, we design an algorithm with the ideas of Coverage Efficiency Indicator, Data Balance-aware Dual Adjustment, and Privacy-Aware Rerouting to choose and cluster SIoTs and to determine the aggregator for local training and SIoT routing in each cluster. Simulation results manifest that the proposed algorithm outperforms state-of-the-arts regarding the total cost, model accuracy, and convergence time.

Due to the emergence of mobile 3D devices, multi-view 3D videos are expected to play increasingly important roles in providing immersively interactive experiences to users. Compared with traditional single-view videos, it is envisaged that a multi-view 3D video requires a larger storage space and bandwidth consumption. Nevertheless, efficient caching for multi-view 3D videos in a mobile proxy has not been explored in the literature. In this paper, therefore, we first observe that the storage space can be effectively reduced by leveraging Depth Image Based Rendering (DIBR) in multi-view 3D videos. We then formulate a new cache management problem, named Adaptive View Selection and Cache Operation (AVSCO), and find the optimal policy based on Markov Decision Process (MDP). Afterward, we propose an online algorithm with a guaranteed competitive ratio to support human visual continuity in AVSCO. Then, we devise an approximate MDP to accelerate the computation of MDP by aggregating similar states to reduce the state space. Simulation and prototype implementation results manifest that the proposed algorithms can significantly improve the cache hit rate and reduce the bandwidth consumption for the remote access compared with the existing cache replacement algorithms.

Flow consolidation has been proposed for merging multiple flows from different services into an aggregate flow to remedy the state explosion problem in software-defined networks (SDN). However, we observe that the Quality of Service (QoS) requirements are no longer sustained in aggregate flows since the bandwidth decided by TCP is usually different from the desired rate of each service. Therefore, this article explores an idea to control the rates of only a few service flows so that the rates of all uncontrolled flows allocated by TCP will meet their QoS requirements. We design a new architecture, called Scalable Per-Flow Rate Allocation (SPFRA), and formulate a new optimization problem, termed Scalable Rate Allocation for Aggregate Flows (SRAF), to find a minimum number of controlled flows to increase the scalability of SDN with diverse service requirements. We prove the NP-hardness and inapproximability of SRAF. To solve the problem, we design an algorithm, named Aggregate Flow Selection and Flow Release (AFSFR), to achieve the tightest bound and extend it to support distributed computation and dynamic traffic for instant services. Simulations and implementation on an SDN testbed manifest that AFSFR performs nearly optimally in real networks, and the number of controlled flows can be effectively reduced by 50 percent.

Device-to-device (D2D) communication is one of the promising solutions to improve spectrum efficiency and alleviate the mobile traffic explosion. However, interference mitigation and resource allocation in the underlying cellular network is a challenging task. In this paper, we propose a distributed deep reinforcement learning (DRL) based scheme to solve the interference mitigation and resource allocation problem. According to the channel status, each cellular user (CU) and D2D transmitter (D2D TX) will determine the appropriate reused channel and transmit power to maximize the system throughput. We propose a distributed DRL scheme and integrate two hotbooting algorithms into the scheme to improve the system throughput at the early stage of training. Simulation results show that the proposed distributed DRL with hotbooting outperforms the baselines regarding running time, message overhead, and throughput.

In contrast to traditional videos, the imaging in virtual reality (VR) is 360°, and it consumes larger bandwidth to transmit video contents. To reduce bandwidth consumption, tile-based streaming has been proposed to deliver the focused part of the video, instead of the whole one. On the other hand, the techniques to alleviate cybersickness, which is akin to motion sickness and happens when using digital displays, have not been jointly explored with the tile selection in VR. In this paper, we investigate Tile Selection with Cybersickness Control (TSCC) in an adaptive 360° video streaming system with cybersickness alleviation. We propose an$m$-competitive online algorithm with Cybersickness Indicator (CI) and Video Loss Indicator (VLI) to evaluate instant cybersickness and the total loss of video quality. Moreover, the algorithm exploits Sickness Migration Indicator (SMI) to evaluate the cybersickness accumulated over time and the increase of optical flow to improve the tile quality assignment. Simulations with a real network dataset show that our algorithm outperforms the baselines regarding video quality and cybersickness accumulation.

Social Internet of Things (SIoT) has become an emerging network paradigm, where IoT devices with Artificial Intelligence (AI) and social relations can automatically establish a collaborative group to identify events locally. On the other hand, mobile users can act as ubiquitous and versatile sensors to improve the accuracy of SIoT event detection. In this paper, we explore the SIoT Collaboration with Crowdsourcing (SCC) problem to jointly select SIoT devices and hire users to monitor events and locations with accuracy requirements, while minimizing the total SIoT communication and computation costs and the user hiring cost. We prove that SCC is NP-hard and cannot be approximated by any factor unless P = NP. Then, we propose a new algorithm, Accuracy- and Social-aware SIoT and User Selection (ASSUS), with the idea of Collaborative Tree (CT) and Accuracy Profit (AP), where CT exploits users’ social relations to properly choose intermediate SIoTs. Simulation results manifest that ASSUS can effectively reduce more than 50% of the total cost compared with state-of-the-art algorithms.

New radio (NR) and non-orthogonal multiple access (NOMA) have emerged for more scalable and efficient resource utilization in 5G. NR implements mixed numerology with a flexible radio frame structure to ensure forward compatibility for future services, whereas NOMA allows multiple users with different channel states to share identical radio resources. However, the resource allocation in the NOMA-based mixed numerology system is challenging due to the naturally different shapes of Physical Resource Block (PRB) for NR and the reused locations of PRBs in a radio frame for NOMA. In this paper, we formulate a new optimization problem Multi-Dimensional Resource Allocation Problem (MDRAP) and prove that MDRAP is NP-hard. To solve the problem, we propose an approximation algorithm to maximize the weighted sum rate under the heterogeneity of users. The algorithm includes Zone Displacement to displace the locations of allocated PRBs in different layers of the radio frame, and Zone Allocation to change the location of the bounded rectangles (i.e., zones) for the allocation in each layer. We design Layer Dissimilarity to examine the location and shape of PRBs for avoiding inter-numerology interference between different layers. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms regarding throughput and fairness.

With the emergence of innovative applications for Virtual Reality (VR) in touring, E-commerce, and social activities, high-quality VR video streaming becomes essential. To support numerous wireless VR users, this paper aims to leverage video synthesis techniques to effectively reduce the multicast bandwidth consumption. It synthesizes the view in the video for a user from the one of a nearby user with similar Field of View (FoV), under the virtual distance and view angle constraints. We first formulate a new optimization problem, named VR Content Sharing and Multicasting (VCSM), and prove the NP-hardness. Then, we propose View Sharing Relation Graph (VSRG) to model the synthesis relation between each pair of views. We then design a new algorithm, named Bandwidth-Efficient Multicast with Synthesis (BEMS) to select multicast views and the corresponding MCS in wireless networks. Simulation results show that BEMS can effectively reduce bandwidth consumption by more than 50% compared with state-of-the-art wireless transmission schemes.

Previous research on Segment Routing (SR) mostly focused on unicast, whereas online SDN multicast with segment trees supporting IETF dynamic group membership has not been explored. Compared with unicast SR, online SDN multicast with segment trees is more challenging since finding an appropriate size, shape, and location for each segment tree is crucial to deploy it in more multicast trees. In this paper, we explore Multi-tree Multicast Segment Routing (MMSR) to jointly minimize the bandwidth consumption and forwarding rule updates over time by leveraging segment trees. We prove MMSR is NP-hard and design an online competitive algorithm, named Segment Tree Routing and Update Scheduling (STRUS) to achieve the tightest bound. STRUS includes Segment Tree Merging and Segment Tree Pruning to merge smaller overlapping subtrees into segment trees, and then tailor them to serve more multicast trees. We design Stability Indicator and Reusage Indicator to carefully construct segment trees at the backbone of multicast trees and reroute multicast trees to span more segment trees. Simulation and implementation on real SDNs with YouTube traffic manifest that STRUS outperforms state-of-the-art algorithms regarding the total cost and TCAM usage. Moreover, STRUS is practical for SDN since its running time is about 1 second, even for massive networks with thousands of nodes.

Artificial intelligence (AI) on chips has recently driven the expansion of the Social Internet of Things (SIoT), where a group of SIoT devices with social relations can collaboratively identify and handle local events without the help of remote servers. On the other hand, mobile-edge computing (MEC) is a favorable way to locally process SIoT data for reducing data transmission and computation among SIoT devices and backhaul networks. Nevertheless, the load sharing among SIoT devices, MEC, and remote servers brings about new challenges for the communication and computation tradeoff, cross-layer design in SIoT, and forwarding and aggregation tradeoff. To tackle these issues, we formulate a new optimization problem, namely, SIoT collaborative group and device selection problem (SCGDSP), and prove the NP-hardness. We first explore the intrinsic properties of a fundamental SCGDSP case by finding the optimal collaborative group for each user request. Then, we design an approximation algorithm for the general SCGDSP that first evaluates candidate collaborative groups under different social relations, and then selects the collaborative groups and SIoT devices properly. For scalability, the proposed algorithm also supports dynamic user requests and can be distributionally deployed in massive networks enabling collaborative MEC. Moreover, it also sustains local SIoT services, where the computation only involves SIoT devices and MEC servers. Simulation results demonstrate that effective SIoT and collaborative group selection (ESCGS) can reduce by more than 50% of the total communication and computation costs compared with baseline schemes in the real networks from topology zoo. Moreover, the distributed ESCGS reduces by 87% of the running time with total 16.5-MB message overhead, requiring no more than 0.05-ms transmission delay in a 100-Gb/s backbone network with eight MEC servers, 1000 SIoTs, and 800 monitored locations.

With the emergence of virtual reality (VR) like holographic telepresence, it is envisaged that the current networks may be overwhelmed due to the higher bandwidth demand to support high-resolution videos. On the other hand, Camera Blending Field (CBF) has been proposed to restore the occluded regions of cameras to enhance the user’s Quality of Experience (QoE) in VR. In this paper, we explore the source selection to maximize the user’s QoE under the network capacity and 3D reconstruction constraints. We first formulate a new optimization problem, named Source Selection for Real-time Telepresence with Occlusion (SSRTO), with the objective of QoE maximization. Then, we prove the NP-hardness and propose a new algorithm, Maximum Quality of Complete Model (MQCM), to maximize the user’s QoE by examining the proximity of cameras on the built Angle Directed Graph (ADG) and Neighbor Directed Graph (NDG) for reconstructing the complete 3D model. Simulation results show that MQCM can effectively improve the user’s QoE by more than 100% compared with the baseline schemes.

Internet of Things (IoT) has been regarded as one of the most significant network paradigms in the future. For IoT, it is crucial to ensure the correctness of detection which includes the factors of accuracy and precision. On the other hand, Mobile Edge Computing (MEC) has emerged as a promising way to process big IoT data at the network edge so as to reduce the computation and transmission energy in the networks. In this paper, we explore the energy minimization problem in MEC networks by considering both the accuracy and precision requirements of IoT. Specifically, given 1) a set of IoT devices, 2) a set of observed targets, 3) an MEC network, 4) the energy consumption model, and 5) the accuracy and precision requirements, we formulate a new optimization problem, named Accuracy and Precision-Aware IoT Device Selection (APAIDS), to minimize the overall energy consumption in MEC networks. We prove the NP-hardness of APAIDS and then propose a new algorithm, named Energy Efficient Device and MEC Server Selection (EDMS), to minimize energy consumption by jointly selecting IoT devices, configuring MEC association, and selecting processing servers for dealing with the data of each target. Finally, we evaluate EDMS on two real networks. In comparison with the baseline schemes, the results manifest that the overall energy consumption can be reduced by more than 60%.

Mobile Edge Computing (MEC) has recently emerged as a primary candidate for big data processing to reduce the latency and jitter. On the other hand, Software-Defined Internet of Things (SD-IoT) has been proposed to effectively and flexibly collect and process big IoT data. Nevertheless, minimizing the energy consumption in SD-IoT with big data processing (e.g., data aggregation and reconstruction) has not been explored before. In this paper, therefore, we explore the sensor data selection and routing problem in SD-IoT with big data processing. Specifically, given 1) a set of sensors, 2) a set of observed locations, 3) the network topology, and 4) the energy consumption model of big data processing and forwarding, we formulate a new optimization problem, named Sensor Data Selection, Processing, and Routing Problem (SDSPRP), to minimize the total energy consumption in SD-IoT. We prove that the emphasized problem is NP-hard and inapproximable within O(log|K|). To solve the problem, we propose an αlog|K|- approximation algorithm, called Energy Efficient Sensor Selection and Routing (ESR), to minimize the energy consumption by jointly considering the sensor selection and the energy consumption in traffic engineering and data processing. The proposed algorithm is evaluated on two real networks, and the results manifest that the energy consumption in SD- IoT can be reduced by more than 56%.

Most scheduling algorithms in the literature for cellular networks are concerned with throughput, fairness, or cost optimization. Recently, however, wireless surveillance in cellular networks has become increasingly important, and more and more institutions and companies have adopted commercial cellular surveillance cameras due to their low installation cost and the wide network coverage. In this paper, therefore, we first explore the resource allocation problem for a multi-camera surveillance system in cellular networks. We minimize the number of allocated resource blocks (RBs) while simultaneously ensuring the coverage requirement for the surveillance system in cellular networks. Specifically, we first describe our system model and then formulate the Camera Set Resource Allocation Problem (CSRAP). Next, we prove that the problem is NP-hard and inapproximable within ln n, where n is the number of surveillance targets. To solve the problem, we propose an approximation algorithm for the general case of CSRAP and then we find the optimal solutions of three deployments of cameras in the Manhattan Street Network to find the intrinsic characteristics of camera selections. The simulation results, based on two real surveillance maps and synthetic datasets, show that the number of allocated RBs can be effectively reduced compared to the existing approach for cellular networks.

Most prior algorithms for influence maximization focused are designed for Online Social Networks (OSNs) and require centralized computation. Directly deploying the above algorithms in distributed Mobile Social Networks (MSNs) will overwhelm the networks due to an enormous number of messages required for seed selection. In this paper, therefore, we design a new cross-layer strategy to jointly examine MSN and mobile ad hoc networks (MANETs) to facilitate efficient seed selection, by extracting a subset of nodes as agents to represent nearby friends during the distributed computation. Specifically, we formulate a new optimization problem, named Agent Selection Problem (ASP), to minimize the message overhead transmitted in MANET. We prove that ASP is NP-Hard and design an effectively distributed algorithm. Simulation results in real and synthetic datasets manifest that the message overhead can be significantly reduced compared with the existing approaches.

Wireless surveillance in cellular networks has become increasingly important, while commercial LTE surveillance cameras are also available nowadays. Nevertheless, most scheduling algorithms in the literature are throughput, fairness, or profit-based approaches, which are not suitable for wireless surveillance. In this paper, therefore, we explore the resource allocation problem for a multi-camera surveillance system in 3GPP Long Term Evolution (LTE) uplink (UL) networks. We minimize the number of allocated resource blocks (RBs) while ensuring the coverage requirement for surveillance systems in LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an Integer Linear Programming formulation for general cases to find the optimal solution. Moreover, we present a baseline algorithm and devise an approximation algorithm to solve the problem. Simulation results based on a real surveillance map and synthetic datasets manifest that the number of allocated RBs can be effectively reduced compared to the existing approach for LTE networks.

Due to the proliferation of applications for the Internet of Things, an increasing number of machine to machine (M2M) devices are being deployed. In particular, one of the M2M applications, video surveillance, has been widely discussed. Long Term Evolution (LTE), which can provide a high rate of data transmission and wide range of coverage, is a promising standard to serve as an M2M video surveillance system. In this paper, we study a performance maximization problem in an LTE video surveillance system. Given a set of objects and a set of cameras, each camera has its own performance grade and its own coverage. The goal is to maximize the performance of the system by allocating limited resources to cameras while all objects should be monitored by the selected cameras. We propose a heuristic method to select the cameras and allocate resources to them to solve the problem. Moreover, to reduce the load of the LTE system, a dynamic adjustment method is also proposed.

IoT browser is a useful tool for the Internet of Things (IoT), which provides a novel way for people to interact with daily life objects through the Internet. Comparing with traditional web browsing environment, IoT browsing environment has some distinctive features such as the way of interacting with the physical objects, the importance of spatial-temporal information of objects, and the necessity of resource reuse. In this demonstration, we present a prototype of a proposed IoT browsing system architecture with a semantic-based learning-capable middleware for IoT browser and demonstrate the applicability of the system in a browsing scenario. The learning scheme is able to suggest users the next possible events when the users design their own service flows through web interface. The prototype shows that with the help of the IoT browsing system, heterogeneous devices can cooperate with each other to provide IoT services.