In this paper, we propose a secure lightweight and thing-centered IoT communication system based on MQTT, SecT, in which a device/thing authenticates users. Compared with a server-centered IoT system in which a cloud server authenticates users, a thing-centered system preserves user privacy since the cloud server is primarily a relay between things and users and does not store or see user data in plaintext. The contributions of this work are three-fold. First, we explicitly identify critical functionalities in bootstrapping a thing and design secure pairing and binding strategies. Second, we design a strategy of end-to-end encrypted communication between users and things for the sake of user privacy and even the server cannot see the communication content in plaintext. Third, we design a strong authentication system that can defeat known device scanning attack, brute force attack and device spoofing attack against IoT. We implemented a prototype of SecT on a $10 Raspberry Pi Zero W and performed extensive experiments to validate its performance. The experiment results show that SecT is both cost-effective and practical. Although we design SecT for the smart home application, it can be easily extended to other IoT application domains.

Visual (V) surveillance systems are extensively deployed and becoming the largest source of big data. On the other hand, electronic (E) data also plays an important role in surveillance and its amount increases explosively with the ubiquity of mobile devices. One of the major problems in surveillance is to determine human objects' identities among different surveillance scenes. Traditional way of processing big V and E datasets separately does not serve the purpose well because V data and E data are imperfect alone for information gathering and retrieval. Matching human objects in the two datasets can merge the good of the two for efficient large-scale surveillance. Yet such matching across two heterogeneous big datasets is challenging. In this paper, we propose an efficient set of parallel algorithms, called EV-Matching, to bridge big E and V data. We match E and V data based on their spatiotemporal correlation. The EV-Matching algorithms are implemented on Apache Spark to further accelerate the whole procedure. We conduct extensive experiments on a large synthetic dataset under different settings. Results demonstrate the feasibility and efficiency of our proposed algorithms.

Massive visual traffic data have become available recently. Though it opens the realm of intelligent traffic analysis, processing the data in a timely manner is difficult yet critical to time sensitive decisions, which are typical to traffic related management. In this paper, we study time-bounded aggregation analytics on large visual traffic data including traffic images and videos. We first find that current MapReduce framework can not work well due to two challenges: first, significant dual diversities exist on data distributions and processing time; second, apriori knowledge on these distributions and time costs are not always available. However, we also observe spatial and temporal locality on data values and processing time. Based on the examination, we design Traffic At-a-Glance (TaG), an augmented MapReduce framework for time-bounded traffic analytics jobs. Particularly, we propose a novel sampling algorithm that exploits traffic data localities and stratifies samples based on data distributions and processing time. It runs in an iterative, adaptive manner without apriori knowledge. Moreover, we propose a heuristic scheduling algorithm with considerations of batch processing overhead. Further, we refine the load balancing mechanism based on data processing time locality to respect job time bounds. In addition, we extend TaG to well handle traffic videos by sampling video data based on motion information encoded in the videos. We implement TaG on Hadoop and conduct extensive experiments on a large visual traffic dataset. The evaluations on different data sizes show TaG is able to achieve high accuracy within time bounds.

Massive visual traffic data have become available recently, which provides an opportunity for intelligent traffic analysis. Timely processing is particularly necessary for traffic analysis. In this paper, we study time-bounded aggregation analytics on large visual traffic data. We first find that current MapReduce framework can not work well due to two challenges: first, significant dual diversities exist on data distributions and processing time; second, no apriori knowledge on these distributions and time costs is available. However, we also observe spatial and temporal locality on data values and processing time. Based on the examination, we design TaG, an augmented MapReduce framework for time-bounded traffic analytics jobs. Particularly, we propose a novel sampling algorithm that exploits traffic data localities and stratifies samples based on data distributions and processing time. It runs in an iterative, adaptive manner without apriori knowledge. Moreover, we propose a heuristic scheduling algorithm with considerations of batch processing overhead. Further, we refine load balancing mechanism based on data processing time locality to respect job time bounds. We implement TaG on Hadoop and conduct extensive experiments on a large traffic image dataset. The evaluations on different data sizes show TaG is able to achieve high accuracy within different time bounds.

Many mobile robots are expected to work for or interact with humans indoors in applications such as guided shopping, policing, and senior care. Mobile robots' sensors alone are insufficient in order to realize these applications, infrastructural support is needed. Existing support for mobile robots requires heavy or expensive infrastructures with limited scalability or deployment of unsightly lines or magnetic strips. This paper presents S-Mirror, a novel approach that "reflects" various ambient signals towards mobile robots, greatly extending their sensing abilities. S-Mirror forms a network of S-Mirror nodes that mainly reflect visual signals (as well as electronic and acoustic signals) to assist mobile robots. To illustrate the advantages of S-Mirror, we develop a localization approach for mobile robots that integrates S-Mirror and robots' on-board motion sensors. We implement S-Mirror and a mobile robot prototype on commercial off-the-shelf hardware. Our real-world experimental validation shows that S-Mirror achieves accurate timely localization with low network bandwidth consumption as well as robustness and scalability to many mobile robots.

There is a fast growing number of public spaces offering Wi-Fi access to meet the rising demands for the Internet. It is common for such service to be offered to users at no charge or for a flat fee. Both situations provide very little incentive for Wi-Fi providers to offer better service to the users. Similarly, Wi-Fi providers pay a monthly flat-rate to ISP for Internet access, which does not incentivize ISP to offer better service to Wi-Fi users. As a result, Wi-Fi users may experience poor Internet connection when network becomes congested during peak hours. In this paper, we propose a dynamic pricing mechanism for both ISP and Wi-Fi providers in order to give mobile Wi-Fi users better service, while providing economic incentive for both ISP and Wi-Fi provider.

This paper focuses on designing a distributed medium access control algorithm for fairly sharing network resources among contending stations in an 802.11 wireless network. Because the notion of fairness is not universal and there lacks a rigorous analysis on the relationships among the four types of most popular fairness criteria, we first mathematically prove that there exist certain connections between these types of fairness criteria. We then propose an efficient medium access algorithm that aims at achieving time fairness and throughput enhancement in a fully distributed manner. The core idea of our proposed algorithm lies in that each station needs to select an appropriate contention window size so as to fairly share the channel occupancy time and maximize the throughput under the time fairness constraint. The derivation of the proper contention window size is addressed rigorously. We evaluate the performance of our proposed algorithm through an extensive simulation study, and the evaluation results demonstrate that our proposed algorithm leads to nearly perfect time fairness, high throughput, and low collision overhead.

Multi-hop coverage extension can be utilized as a feasible approach to facilitating uncovered users to get Internet service in public area WLANs. In this paper we introduce a relay-union network (RUN), which refers to a public area WLAN in which users often wander in the same area and have the ability to provide data forwarding services for others. We develop a RUN framework to model the cost of providing forwarding services and the utility obtained by gaining services. The objective of the RUN is to maximize the total Quality of Cooperation (QoC) of users in the RUN. Two optimal bandwidth allocation schemes are proposed for both free and dynamic bandwidth demand models. To make our scheme more pragmatic, we then consider a more practical scenario in which the bandwidth capacity of the relays and the minimum demand of the clients are bounded. We prove that the problems under both the single relay and the multi-relay scenario are NP-hard. Three heuristic algorithms are proposed to deal with bandwidth allocation and relay-client association. We also propose a distributed signaling protocol and divide the centralized MRMC algorithm into three distributed ones to better adapt for real network environment. Finally, extensive simulations demonstrate that our RUN framework can significantly improve the efficiency of cooperation in the long term.

In mobile ad-hoc cognitive radio networks (CRNs), end-to-end paths with available spectrum bands for secondary users may exist temporarily, or may never exist, due to the dynamism of the primary user activities. Traditional CRN routing algorithms, which typically ignore the intermittent connectivity of network topology, and traditional mobility-assisted routing algorithms, which generally overlook the spectrum availability, are obviously unsuitable. To tackle this challenge, we propose a Mobility-Assisted Routing algorithm with Spectrum Awareness (MARSA) to select relays based on not only the probability that a node meets the destination but also the chance at which there exists at least one available channel when they meet. To the best of our knowledge, this paper is the first to bring the idea of mobility-assisted routing to deal with the intermittently connected attribute of mobile ad-hoc CRNs, and the first to enhance the mobility-assisted routing by considering the temporal , spatial, and spectrum domains at the same time. Our simulation results demonstrate the superiority of MARSA over traditional algorithms in intermittently connected mobile CRNs.

Mobile P2P networking is an enabling technology for mobile devices to self-organize in an unstructured style and communicate in a peer-to-peer fashion. Due to user mobility and/or the unrestricted switching on/off of the mobile devices, links are intermittently connected and end-to-end paths may not exist, causing routing a very challenging problem. Moreover, the limited wireless spectrum and device resources together with the rapidly growing number of portable devices and amount of transmitted data make routing even harder. To tackle these challenges, the routing algorithms must be scalable, distributed, and light-weighted. Nevertheless, existing approaches usually cannot simultaneously satisfy all these three requirements. In this paper, we propose two opportunistic routing algorithms for intermittently connected mobile P2P networks, which exploit the spatial locality, spatial regularity, and activity heterogeneity of human mobility to select relays. The first algorithm employs a depth-search approach to diffuse the data towards the destination. The second one adopts a depth-width-search approach in a sense that it diffuses the data not only towards the destination but also to other directions determined by the actively moving nodes (activists) to find better relays. We perform both theoretical analysis as well as a comparison based simulation study. Our results obtained from both the synthetic data and the real world traces reveal that the proposed algorithms outperform the state-of-the-art in terms of delivery latency and delivery ratio.

This paper focuses on designing a distributed medium access control algorithm that aims at achieving time fairness among contending stations and throughput maximization in an 802.11 wireless LAN. The core idea of our proposed algorithm lies in that each station needs to select an appropriate contention window size so as to fairly share the channel occupancy time and maximize the throughput under the time fairness constraint. The derivation of the proper contention window size is presented rigorously. We evaluate the performance of our proposed algorithm through an extensive simulation study, and the evaluation results demonstrate that our proposed algorithm leads to nearly perfect time fairness, high throughput, and low collision overhead.

In this paper, we propose a systematic approach to designing and deploying a RFID Assisted Navigation System (RFID-ANS) for VANETs. RFID-ANS consists of passive tags deployed on roads to provide navigation information while the RFID readers attached to the center of the vehicle bumper query the tag when passing by to obtain the data for navigation guidance. We analyze the design criteria of RFID-ANS and present the design of the RFID reader in detail to support vehicles at high speeds. We also jointly consider the scheduling of the read attempts and the deployment of RFID tags based on the navigation requirements to support seamless navigations. The estimation of the vehicle position and its accuracy are also investigated.

This paper addresses the problem of target counting based on the Monte Carlo simulation. We rely on an Accept-Reject process to guide the placement of virtual targets in a virtual sensor field, which has exactly the same sensor layout as the real one. The objective of this construction is to generate a virtual target energy landscape whose shape is close enough to an energy landscape estimated from the real sensor readings. Based on the number of virtual targets placed on the virtual field and the total virtual and real target energy volumes, the number of real targets can be estimated. We consider both single-epoch and multi-epoch sensor readings and our theoretical analysis indicates that by exploiting the information from multiple epochs, our approach yields a target count that approximately converges to the true target count when the number of epochs is large enough. Extensive comparison based simulation study has been performed and the results verify the effectiveness of our target counting algorithms.

The ability of discovering neighboring nodes, namely neighbor discovery, is essential for the self-organization of wireless ad hoc networks. In this paper, we propose a history-aware adaptive back off algorithm for neighbor discovery assuming collision detection and feedback mechanisms. Given successful discovery feedback, undiscovered nodes can adjust their contention window. With collision feedback and historical information, only transmission nodes enter the re-contention process, and decrease their contention window to accelerate neighbor discovery process after collision. Then, we give theoretical analysis of our algorithm on the discovery time and energy consumption, and derive the optimal size of contention windows by two rounds of optimization. Finally, we validate our theoretical analysis by simulations, and show the performance improvement over existing algorithms.

One promising switching technology for wavelength-division multiplexing optical networks is optical burst switching (OBS). However, there are major deficiencies of OBS. (1) The delay offset between a control message and its corresponding data burst is based on the diameter of a network. This affects network efficiency, quality-of-service, and network scalability.( 2) OBS adopts one-way resource reservation scheme, which causes frequent burst collision and, thus, burst loss. We address the above two important issues in OBS. In particular, we study how to improve the performance of delay and loss in OBS. To reduce the end-to-end delay, we propose a hybrid switching scheme. The hybrid switching is a combination of lightpath switching and OBS switching. A virtual topology design algorithm based on simulated annealing to minimize the longest shortest path through the virtual topology is presented. To minimize burst collision and loss, we propose a new routing algorithm, namely, p-routing, for OBS network. The p-routing is based on the wavelength available probability. A path that has higher available probability is less likely to drop bursts due to collision. The probability-based p-routing can reduce the volatility, randomness, and uncertainty of one-way resource reservation. Our studies show that hybrid switching and p-routing are complementary and both can dramatically improve the performance of OBS networks.

We study the problem of wavelength assignment for multicast in order to maximize the network capacity in all-optical wavelength-division multiplexing networks. The motivation behind this work is to minimize the call blocking probability by maximizing the remaining network capacity after each wavelength assignment. While all previous studies on the same objective concentrate only on the unicast case, we study the problem for the multicast case. For a general multicast tree, we prove that the multicast wavelength assignment problem of maximizing the network capacity is NP-hard and propose two efficient greedy algorithms. We also study the same problem for a special network topology, a bidirectional ring network, which is practically the most important topology for optical networks. For bidirectional ring networks, a special multicast tree with at most two leaf nodes is constructed. Polynomial time algorithms for multicast wavelength assignment to maximize the network capacity exist under such a special multicast tree with regard to different splitting capabilities. Our work is the first effort to study the multicast wavelength assignment problem under the objective of maximizing network capacity.

The next generation multimedia applications such as video conferencing and HDTV have raised tremendous challenges on the network design, both in bandwidth and service. As wavelength-division-multiplexing (WDM) networks have emerged as a promising candidate for future networks with large bandwidth, supporting efficient multicast in WDM networks becomes eminent. Different from the IP layer, the cost of multicast at the WDM layer involves not only bandwidth (wavelength) cost, but also wavelength conversion cost and light splitting cost. It is well known that the optimal multicast problem in WDM networks is NP-hard. In this paper, we develop an efficient approximation algorithm consisting of two separate but integrated steps: multicast routing and wavelength assignment. We prove that the problem of optimal wavelength assignment on a multicast tree is not NP-hard; in fact, an optimal wavelength assignment algorithm with complexity of O(NW) is presented. Simulation results have revealed that the optimal wavelength assignment beats greedy algorithms by a large margin in networks using many wavelengths on each link such as dense wavelength-division-multiplexing (DWDM) networks. Our proposed heuristic multicast routing algorithm takes into account both the cost of using wavelength on links and the cost of wavelength conversion. The resulting multicast tree is derived from the optimal lightpaths used for unicast.

The next generation multimedia applications such as video conferencing and HDTV have raised tremendous challenges on the network design, both in bandwidth and service. As wavelength division multiplexing (WDM) networks have emerged as a promising candidate for future networks with large bandwidth, supporting efficient multicast in WDM networks becomes eminent. Different from the IP layer, the cost of multicast at the WDM layer involves not only bandwidth (wavelength) cost but also wavelength conversion cost. In this paper, we study the multicast problem in WDM networks supporting wavelength conversion. In particular, we address the problem of minimizing cost of wavelength conversion in a multicast tree with upper bound on the number of conversions from source to destinations. An efficient optimal wavelength assignment algorithm is presented.

When a computer network is used to support real time applications, meeting delay requirements is a key issue that has to be addressed. We study high performance networks with wormhole routing and investigate their performance in terms of meeting message delay constraints. We found that in this kind of network, preemption control performed at each node plays an important role in supporting real time traffic. In wormhole networks, this may result in unbounded packet blocking time, making it very difficult to efficiently support real time applications. To overcome this problem, we propose both transparent and non-transparent preemption schemes. Simulations show that combining proposed preemption schemes with other priority based schemes can outperform the conventional priority based schemes by a large margin, and the schemes have stable performance in large scale networks.

We study connection-oriented service in an FDDI-ATM-FDDI heterogeneous network for real-time applications. We design and analyze an algorithm for connection admission control (CAC) for such a network. Upon a request of connection establishment, the CAC determines if the worst case delays of the requesting and existing connections can be satisfied given the available network resources. If so, the CAC allocates appropriate network resources to the requesting connection. The process of allocating resources for homogeneous networks (e.g. FDDI-only or ATM-only) may not be applied directly to a heterogeneous network environment (e.g. FDDI-ATM-FDDI network) because heterogeneity adds more complexity to the process. Hence resource allocation in a heterogeneous network needs more careful analysis than its homogeneous counterpart. We propose a CAC algorithm that will, by proper parameter tuning, allocate sufficient but not excessive network resources to the requesting connection in an FDDI-ATM-FDDI network. We show that the system can achieve satisfactory performance with this CAC algorithm. Our approach is compatible with current network standards and hence can be readily used in practical systems.

We study high performance networks with wormhole routing and investigate their performance in terms of meeting message delay constraints. Traditional system uses unregulated greedy transmission control. This may result in unfairness of network access and unbounded packet blocking time, making it very difficult to efficiently support real-time applications. To overcome this problem, we propose a regulated transmission control method in which packet transmission at the source is regulated and hence unnecessary network contention is eliminated The regulated method is a generalization of the unregulated method and can be easily implemented in most of the commercially available networks.

FDDI-Based Reconfigurable Networks have an architecture that is suitable for delivering messages that have hard real-time constraints as well as certain fault-tolerance requirements. This architecture uses multiple FDDI networks to connect hosts and provides for automatic reconfiguration to maintain high network bandwidth in spite of faults. An important open problem is how resources in such networks should be managed in order to guarantee that the fault-tolerant real-time requirements of messages are met. This paper presents an efficient and practical solution to this problem. Our solution consists of off-line and on-line components. On-line management deals with run-time manipulation of messages and network resources. A message grouping approach simplifies on-line management. Off-line management deals with message grouping, bandwidth allocation and schedulability verification. Three approaches are investigated: spatial redundancy, temporal redundancy and an integrated approach. It is shown that the integrated approach has the best performance. Our solution is compatible with the FDDI and SAFENET standards.

We study the problem of guaranteeing synchronous message deadlines in token ring networks where the timed token medium access control protocol is employed. Synchronous bandwidth, defined as the maximum time for which a node can transmit its synchronous messages every time it receives the token, is a key parameter in the control of synchronous message transmission. To ensure the transmission of synchronous messages before their deadlines, synchronous capacities must be properly allocated to individual nodes. We address the issue of appropriate allocation of the synchronous capacities. Several synchronous bandwidth allocation schemes are analyzed in terms of their ability to satisfy deadline constraints of synchronous messages. We show that an inappropriate allocation of the synchronous capacities could cause message deadlines to be missed, even if the synchronous traffic is extremely low. We propose a scheme, called the normalized proportional allocation scheme, which can guarantee the synchronous message deadlines for synchronous traffic of up to 33% of available utilization.>

The problem of guaranteeing synchronous message deadlines in communication networks using the timed token medium access control protocol is studied. To ensure the transmission of synchronous messages before their deadlines, synchronous capacities must be properly allocated to individual nodes. A class of local synchronous capacity allocation schemes, which allocate the synchronous capacity to a node without using information about messages on the other nodes, is developed, analyzed, and evaluated in terms of the ability of the schemes to guarantee message deadlines. It is shown that one of the local allocation schemes proposed can achieve the same performance as that of the best global allocation scheme known to date.>

The problem of guaranteeing synchronous message deadlines in token ring networks in which the timed token medium access control protocol is used is discussed. Synchronous capacity, defined as the maximum time for which a node can transmit its synchronous messages every time it receives the token, is a key parameter in the control of synchronous message transmission. To ensure the transmission of synchronous messages before their deadlines, synchronous capacities must be properly allocated to individual nodes. Several synchronous capacity allocation schemes are analyzed in terms of their ability to satisfy deadline constraints of synchronous messages. It is shown that an inappropriate allocation of the synchronous capacities could cause message deadlines to be missed, even if the synchronous traffic is extremely low. The normalized proportional allocation scheme, which can guarantee the synchronous message deadlines for synchronous traffic of up to 33% of available utilization is proposed.>

The authors study the problem of transmitting synchronous messages before their deadlines in communication networks where the timed token medium access control protocol is employed. Synchronous capacity, defined as the maximum time for which a node can transmit its synchronous message every time it receives the token, is a key parameter in the control of synchronous message transmission. To ensure the transmission of synchronous messages before their deadlines, synchronous capacities must be property allocated to individual nodes. The authors develop and analyze an optimal synchronous capacity allocation scheme. An optimal scheme can allocate the synchronous capacities in such a way that the synchronous message deadlines are guaranteed if there exists any allocation scheme that can do so. The optimality of the allocation scheme proposed here is formally proved, and the bounds for its worst case achievable utilization are derived.>