Recent advances in wireless communications and electronics has enabled the development of machine-to-machine (M2M) communications. This communication paradigm has been expected as an automated control and report solution for smart grid. The smart grid enables customers and operators to utilize the collected usage information from a large number of meters with transceivers for efficiency and safety. In this paper, we introduce architecture, requirements and challenges of M2M communications for smart grid. We extract technical issues that should be resolved in M2M communications to support the smart grid via third-generation partnership project (3GPP) cellular networks. We then present the current state of the art of research results to deal with such issues. Finally, we outline the open research issues.

As a new business concept, machine-to-machine (M2M) communications are born from original telemetry technology with the intrinsic features of automatic data transmissions and measurement from remote sources typically by cable or radio. M2M includes a number of technologies that need to be combined in a compatible manner to enable its deployment over a broad market of consumer electronics. In order to provide better understanding for this emerging concept, the correlations among M2M, wireless sensor networks, cyber-physical systems (CPS), and internet of things are first analyzed in this paper. Then, the basic M2M architecture is introduced and the key elements of the architecture are presented. Furthermore, the progress of global M2M standardization is reviewed, and some representative applications (i.e., smart home, smart grid and health care) are given to show that the M2M technologies are gradually utilized to benefit people's life. Finally, a novel M2M system integrating intelligent road with unmanned vehicle is proposed in the form of CPS, and an example of cyber-transportation systems for improving road safety and efficiency are introduced.

Machine to machine (M2M) communications is the hottest issue in the standardization and industry area, it is also defined as machine-type communication (MTC) in release 10 of the 3rd Generation Partnership Project (3GPP). Recently, most research have focused on congestion control, sensing, computing, and controlling technologies and resource management etc., but there are few studies on security aspects. In this paper, we first introduce the threats that exist in M2M system and corresponding solutions according to 3GPP. In addition, we present several new security issues including group access authentication, multiparty authentication and data authentication, and propose corresponding solutions through modifying existing authentication protocols and cryptographic algorithms, such as group authentication and key agreement protocol used to solve group access authentication of M2M, proxy signature for M2M system to tackle authentication issue among multiple entities and aggregate signature used to resolve security of small data transmission in M2M communications.

The aim of this article is to provide a study on the issue of inventory inaccuracy and to show the manner in which RFID technology can improve the inventory management performance. The objective of inventory control is to monitor the stock flow of merchandises in order to understand the operating profit and loss. A proper mechanism of inventory control could be made to help the profitability. As RFID is applied to inventory control, it can improve efficiency, enhance accuracy and achieve security. In this paper, we introduce the evolution of different mechanisms of inventory control with RFID system-counting method, collect-all method, and continuous monitoring method. As for improving the accuracy of inventory check during business hours, continuous monitoring is the solution. We introduce the infrastructure of the RFID inventory management system based on M2M architecture can make the inventory be efficiently monitored with instant warnings.

To address the need for autonomous control of remote and distributed mobile systems, Machine-to-Machine (M2M) communications are rapidly gaining attention from both academia and industry. M2M communications have recently been deployed in smart grid, home networking, health care, and vehicular networking environments. This paper focuses on M2M communications in the vehicular networking context and investigates areas where M2M principles can improve vehicular networking. Since connected vehicles are essentially a network of machines that are communicating, preferably autonomously, vehicular networks can benefit a lot from M2M communications support. The M2M paradigm enhances vehicular networking by supporting large-scale deployment of devices, cross-platform networking, autonomous monitoring and control, visualization of the system and measurements, and security. We also present some of the challenges that still need to be addressed to fully enable M2M support in the vehicular networking environment. Of these, component standardization and data security management are considered to be the most significant challenges.

The design, development and deployment of Mobile Agent (MA) systems for high-level inference and surveillance in wireless sensor networks and RFID systems have drawn increasing attention in the past decade. To answer how the state-of-the-art of MA in a wide range of ubiquitous and sensor environments is, this paper investigates the current progress of MA. It proposes a taxonomy, by which MA systems in ubiquitous computing environments are decomposed and discussed. Then, this paper provides insights into the strengths and weaknesses of existing efforts. Finally, it presents a series of solutions from the viewpoint of various roles of MA in ubiquitous environments and situations.

Ubiquitous computing called pervasive one is based on the thought of pervading ability of computation in daily life applications. In other words, it aims to include computation in devices such as electronic equipment and automobiles. This has led to disengagement of computers from desktop form. Accordingly, the notice in ubiquitous computing being taken of a world steeped in remote and wireless computer-based-services. Handheld and wearable programmed devices such as sense and control appliances are such devices. This advancement is rapidly moving domestic tasks and life from device-and-human communication to the device-and-device model. This model called Machine to Machine (M2M) has led to acceleration of developments in sciences such as nano-science, bio-science, and information science. As a result, M2M led to appearance of applications in various fields such as, environment monitoring, agricultural, health care, logistics, and business. Since it is envisaged that M2M communications will play a big role in the future in all wireless applications and will be emerged as a progressive linkage for next-generation communications, this paper aims to consider how much M2M architectures can realize ubiquitous computing in daily life applications. This is carried out after acquainting and initiating readers with M2M architectures and arguments for M2M. Some of the applications was not achievable before but are becoming viable owing to emergence of M2M communications.

In this paper, a new compressive feedback (CF) scheme based on distributed compressed sensing (DCS) for time-corrected MIMO channel is proposed. First, the channel state information (CSI) is approximated by using a subspace matrix, then, the approximated CSI is compressed using a compressive matrix. At the base station, the approximated CSI can be robust recovered with simultaneous orthogonal matching pursuit (SOMP) algorithm by using forgone CSIs. Simulation results show our proposed DCS-CF method can improve the reliability of system without creating a large performance loss.

We propose a scheme to reduce the overhead associated with channel state information (CSI) feedback required for opportunistic scheduling in wireless access networks. We study the case where CSI is partially overheard by mobiles and thus one can suppress transmitting CSI reports for time varying channels of inferior quality. We model the mechanism of feedback suppression as a Bayesian network, and show that the problem of minimizing the average feedback overhead is NP-hard. To deal with hardness of the problem we identify a class of feedback suppression structures which allow efficient computation of the cost. Leveraging such structures we propose an algorithm which not only captures the essence of seemingly complex overhearing relations among mobiles, but also provides a simple estimate of the cost incurred by a suppression structure. Simulation results are provided to demonstrate the improvements offered by the proposed scheme, e.g., a savings of 63-83% depending on the network size.

Dual carrier frequency offsets (CFOs) occur in multiple-input single-output (MISO)-mode DVB-T2 systems, where signals are transmitted simultaneously from two distributed transmitters in a single frequency network (SFN). In this paper, we first derive an optimal compensation frequency for dual CFOs. We also propose an algorithm that optimizes the compensation frequency for the MISO-mode DVB-T2 application. Its performance is compared with the conventional scheme by using a full DVB-T2 simulator.

Given a video stream delivering system deployed on a multicast tree, which is embedded in a multi-channel multi-radio wireless mesh network, our problem is concerned about how to allocate interference-free channels to tree links and maximize the number of serviced mesh clients at the same time. In this paper, we propose a channel allocation heuristic algorithm based on best-first search and backtracking techniques. The experimental results show that our BFB based CA algorithm outperforms previous methods such as DFS and BFS based CA methods. This superiority is due to the backtracking technique used in BFB approach. It allows previous channel-allocated links to have feasibility to select the other eligible channels when no conflict-free channel can be found for the current link during the CA process. In addition to that, we also propose a tree refinement method to enhance the quality of channel-allocated trees by adding uncovered destinations at the cost of deletion of some covered destinations. Our aim of this refinement is to increase the number of serviced mesh clients. According to our simulation results, it is proved to be an effective method for improving multicast trees produced by BFB, BFS and DFS CA algorithms.

We propose a percolation based M2M networking architecture and its data transmission method. The proposed network architecture can be server-free and router-free, which allows us to operate routing efficiently with percolations based on six degrees of separation theory in small world network modeling. The data transmission can be divided into two phases: routing and data transmission phases. In the routing phase, probe packets will be transmitted and forwarded in the network thus multiple paths are selected and performed based on the constriction of the maximum hop number. In the second phase, the information will be encoded, say, with the fountain codes, and transmitted using the paths generated in the first phase. In such a way, an efficient routing and data transmission mechanism can be built, which allow us to construct a low-cost, flexible and ubiquitous network. Such a networking architecture and data transmission can be used in many M2M communications, such as the stub network of internet of things, and deep space networking, and so on.

To improve the accuracy and enhance the applicability of existing models, this paper proposes a generalized Markov chain model for IEEE 802.11 Distributed Coordination Function (DCF) under the widely adopted assumption of ideal transmission channel. The IEEE 802.11 DCF is modeled by a two dimensional Markov chain, which takes into account unsaturated traffic, backoff freezing, retry limits, the difference between maximum retransmission count and maximum backoff exponent, and limited buffer size based on the M/G/1/K queuing model. We show that existing models can be treated as special cases of the proposed generalized model. Furthermore, simulation results validate the accuracy of the proposed model.

IEEE 802.11 standard has achieved huge success in the past decade and is still under development to provide higher physical data rate and better quality of service (QoS). An important problem for the development and optimization of IEEE 802.11 networks is the modeling of the MAC layer channel access protocol. Although there are already many theoretic analysis for the 802.11 MAC protocol in the literature, most of the models focus on the saturated traffic and assume infinite buffer at the MAC layer. In this paper we develop a unified analytical model for IEEE 802.11 MAC protocol in ad hoc networks. The impacts of channel access parameters, traffic rate and buffer size at the MAC layer are modeled with the assistance of a generalized Markov chain and an M/G/1/K queue model. The performance of throughput, packet delivery delay and dropping probability can be achieved. Extensive simulations show the analytical model is highly accurate. From the analytical model it is shown that for practical buffer configuration (e.g. buffer size larger than one), we can maximize the total throughput and reduce the packet blocking probability (due to limited buffer size) and the average queuing delay to zero by effectively controlling the offered load. The average MAC layer service delay as well as its standard deviation, is also much lower than that in saturated conditions and has an upper bound. It is also observed that the optimal load is very close to the maximum achievable throughput regardless of the number of stations or buffer size. Moreover, the model is scalable for performance analysis of 802.11e in unsaturated conditions and 802.11 ad hoc networks with heterogenous traffic flows.

In the DVB-T2 system with a multiple-input single-output (MISO) transmission mode, Alamouti coded orthogonal frequency division multiplexing (OFDM) signals are transmitted simultaneously from two spatially separated transmitters in a single frequency network (SFN). In such systems, each transmit-receive link may have a distinct carrier frequency offset (CFO) due to the Doppler shift and/or frequency mismatch between the local oscillators. Thus, the received signal experiences dual CFOs. This not only causes dual phase errors in desired data but also introduces inter-carrier interference (ICI), which cannot be removed completely by simply performing a CFO compensation. To overcome this problem, this paper proposes an iterative detection with dual phase errors compensation technique. In addition, we propose a successive-iterative ICI cancellation technique. This technique successively eliminates ICI in the initial iteration by exploiting pre-detected data pairs. Then, in subsequent iterations, it performs a fine interference cancellation using a priori information, iteratively fed back from the channel decoder. In contrast to previous works, the proposed techniques do not require estimates of dual CFOs. Their performances are evaluated via a full DVB-T2 simulator. Simulation results show that the DVB-T2 receiver equipped with the proposed dual phase errors compensation and the successive-iterative ICI cancellation techniques achieves almost the same performance as ideal dual CFOs-free systems, even for large dual CFOs.

Recently various studies on various applications using images taken by mobile phone cameras have been actively conducted. This study proposes a correction method for misrecognition of Korean Texts in signboard images using improved Levenshtein metric. The proposed method calculates distances of five recognized candidates and detects the best match texts from signboard text database. For verifying the efficiency of the proposed method, a database dictionary is built using 1.3 million words of nationwide signboard through removing duplicated words. We compared the proposed method to Levenshtein Metric which is one of representative text string comparison algorithms. As a result, the proposed method based on improved Levenshtein metric represents an improvement in recognition rates 31.5% on average compared to that of conventional methods.

CAPTCHAs are automated tests that are there to avoid misuse of computing and information resources by bots. Typical text-based CAPTCHAs are proven to be vulnerable against malicious automated programs. In this paper, we present an image-based CAPTCHA scheme using easily identifiable human appearance characteristics that overcomes the weaknesses of current text-based schemes. We propose and evaluate two applications for our scheme involving 25 participants. Both applications use same characteristics but different classes against those characteristics. Application 1 is optimized for security while application 2 is optimized for usability. Experimental evaluation shows promising results having 83% human success rate with Application 2 as compared to 62% with Application 1.

Key establishment protocols are fundamental for establishing secure communication channels over public insecure networks. Security must be given the topmost priority in the design of a key establishment protocol. In this work, we provide a security analysis on two recent key establishment protocols: Harn and Lin's group key transfer protocol and Dutta and Barua's group key agreement protocol. Our analysis shows that both the Harn-Lin protocol and the Dutta-Barua protocol have a flaw in their design and can be easily attacked. The attack we mount on the Harn-Lin protocol is a replay attack whereby a malicious user can obtain the long-term secrets of any other users. The Dutta-Barua protocol is vulnerable to an unknown key-share attack. For each of the two protocols, we present how to eliminate their security vulnerabilities. We also improve Dutta and Barua's proof of security to make it valid against unknown key share attacks.

Recently, many malicious users have attacked web browsers using JavaScript code that can execute dynamic actions within the browsers. By forcing the browser to execute malicious JavaScript code, the attackers can steal personal information stored in the system, allow malware program downloads in the client's system, and so on. In order to reduce damage, malicious web pages must be located prior to general users accessing the infected pages. In this paper, a novel framework (JsSandbox) that can monitor and analyze the behavior of malicious JavaScript code using internal function hooking (IFH) is proposed. IFH is defined as the hooking of all functions in the modules using the debug information and extracting the parameter values. The use of IFH enables the monitoring of functions that API hooking cannot. JsSandbox was implemented based on a debugger engine, and some features were applied to detect and analyze malicious JavaScript code: detection of obfuscation, deobfuscation of the obfuscated string, detection of URLs related to redirection, and detection of exploit codes. Then, the proposed framework was analyzed for specific features, and the results demonstrate that JsSandbox can be applied to the analysis of the behavior of malicious web pages.