LoRa technology plays a vital role in IoT applications by enabling low-power and long-range communication. Since the LoRa signal may transmit hundreds of meters to tens of kilometers under different environments, estimating the path loss of LoRa links can bring great benefits to the network, such as device localization and network planning. Recent years have seen some mechanisms to improve the LoRa link estimation by analyzing the environments based on satellite images. However, they may be inaccurate when we consider the actual network in the three-dimensional (3D) scene. This paper proposes 3DLoRa, the first framework to refine LoRa link estimation through exploiting 3D point clouds. We generate a 3D map by conducting semantic segmentation and voxelization based on the point clouds. We then propose both model-based and learning-based methods to estimate the path loss of a link according to the environments obtained from the 3D map. We test the 3D map generation process based on 3D point clouds in a campus area and show an accuracy of about 97% for environment categorization. We also deploy a LoRa network on the campus and collect data to evaluate the performance of link estimation. Experimental results indicate that 3DLoRa can significantly improve the accuracy of link estimation in various environments compared to previous works. Specifically, the model-based method can reduce the estimation error to about$7dBm$with low computational overhead, while the learning-based method can reduce the estimation error to about$2.5dBm$with the cost of higher computational overhead. These results can serve as a reference for how to deploy link estimation methods in different network situations.

WiFi has been widely used for local area networking of devices and Internet access in the past two decades. Many researchers exploit WiFi signals for target sensing through analyzing the Channel State Information (CSI) of signals affected by the target movement. With the development of 6 G Integrated Sensing and Communication (ISAC), some researchers further consider using communication data packets for WiFi sensing. However, all the current works do not analyze the impact of ubiquitous interference on WiFi sensing performance. In this paper, we propose IRSensing, an interference-resistance design to improve the CSI quality under interference in the ISAC scenario, aiming to improve the WiFi sensing performance. IRSensing exploits the overall WiFi packet for CSI optimization. It first measures the interference level of each subcarrier based on variance analysis, then proposes a CSI optimization method based on maximal ratio combining to improve the CSI quality. It finally proposes a practical CSI enhancement process to adapt to complex interference situations in actual networks. We implement IRSensing on a hardware testbed and evaluate its performance under different settings. Experiment results show that it can significantly decrease the activity detection error rate by up to 80% and improve the classification accuracy by up to 15$\%$.

Click-Through Rate (CTR) prediction is one of the core tasks in the recommendation system (RS). In the feature presentation of CTR prediction model, user behavior sequences contain temporal features and implicit interests, which is used for sequential user behavior modeling. There are many deep CTR models on sequential user behavior modeling, while most of them overlook session-based and target-relevant interests. User behavior sequences are composed of multiple sessions, with highly homogeneous user interests within the same session, while user interests across different sessions are heterogeneous. Motivated by the above discoveries, we propose a novel Deep Target Session Interest Network (DTSIN) in this paper. We divide user behavior sequences into multiple original sessions, and append the target item at the end of each original session to obtain target session. Multi-head self-attention is employed to extract target-relevant interests from target sessions. Then, we apply splitting and mean pooling techniques to generate session interests and target interests. Bi-directional GRU (Bi-GRU) is leveraged to capture sequential relationships between session interests. Subsequently, Target Attention (TA) mechanism is used to reallocate the target-related attention scores for session interests, target interests and hidden states. Finally, different features are concatenated and flattened, which is fed into the Multiple Layer Perceptron (MLP) layer for final CTR prediction. Experimental results on the datasets indicate the performance of DTSIN is superior to other state-of-the-art deep CTR models.

This paper proposes a novel scan-then-Iocalize scheme for the unmanned aerial vehicle (UAV) to localize a wireless sensor (WS). In both of the scan and the localizing phases, the UAV estimates its range and/or radial speed to the WS, by exploiting the orthogonal frequency division multiplexing (OFDM) sensing signal. We first derive the UAV's maximally allowable sensing radius to the WS to assure a sufficiently high correct estimation probability. Next, in the scan phase, we design the UAV's scan trajectory along a series of waypoints, where the number of the waypoints and their locations are found by solving the classic region least circle coverage problem. The scan phase ends when the UAV's estimated horizontal range to the WS becomes no larger than the maximally allowable sensing radius. The UAV then begins the localizing phase, where unlike the traditional three-measurement based localization, we propose that only two measurements of the UAV's range and radial speed to the WS are sufficient to localize the WS, but may generate a falsely-found ghost WS. By further identifying the UAV's improper flight angles that generate the ghost WSs, we newly propose the two-measurement based accurate localization algorithm. Finally, various simulation results are provided to validate the localizing performance of our proposed scheme.

Mobile edge computing (MEC) is considered a promising technology to provide low-latency services by keeping computing and other resources physically close to where they are needed. The functions implemented through network function virtualization (NFV) technology in MEC are called virtual network function (VNF) instances, and the deployment and scheduling of VNF instances have always been a hot topic. The deployment refers to deploying instances on the edge servers, while scheduling refers to allocating resources to complete user requests. However, most of the existing works fail to jointly consider the deployment and scheduling of VNF instances, which cannot complete user requests reasonably and efficiently. Besides, the deployment cost can be significantly reduced by making users share the same type of instances instead of assigning one to each user. Therefore, the objective of our article is to investigate the efficient deployment and scheduling of VNF instances that are shared among different users under constraints of user delay and network resources. We first build a VNF instance deployment and scheduling model in MEC networks to study how to minimize cost and maximize network throughput under the constraints of user delay and the computing and storage resources of cloudlets. Then, taking advantage of its sharing feature, we propose a set covering-based efficient deployment and scheduling scheme called SCEDS and evaluate its performance by extensive simulations. The simulation results demonstrate the superiority of our proposed method compared to the existing ones.

With the rapid growth of Internet of Things, the number of heterogeneous wireless devices working in the same frequency band increases dramatically, leading to severe cross-technology interference. To enable coexistence, researchers have proposed a large number of mechanisms to manage interference. However, existing mechanisms have severe modifications in either the physical or MAC (medium access control) layers, making them very different from the standard. In this paper, we design and implement SledZig to boost cross-technology coexistence for low-power devices through both enabling more transmission opportunities and avoiding interference. SledZig is fully compatible with the standard in both physical and MAC layers. It decreases the WiFi signal power on the channel of low-power devices while keeps the WiFi transmission power unchanged, through making constellation points on the overlapped subcarriers have the lowest power, which can be achieved by just encoding the WiFi payload. We implement SledZig on hardware testbed and evaluate its performance under different settings. Experiment results show that SledZig can effectively increase ZigBee transmissions and improve its performance over a WiFi channel under various WiFi data traffic, with as low as 6.94$\%$WiFi throughput loss.

With the deployment of Internet of Things, we have seen many wireless devices working in the crowded unlicensed 2.4GHz band, leading to severe cross-technology coexistence problem. The sensing of signal type and channel can assist wireless devices with more efficient transmission strategy to improve the network performance. In this paper, we present DeepSpectrum, a wireless signal identification system based on convolutional neural networks, to identify the type and estimate the channel of four wireless signals, including Wi-Fi, Zigbee, Lora and Bluetooth. DeepSpectrum consists of a master model for the initial identification of the wireless signals, and an auxiliary model to further improve the accuracy of the Lora channel estimation. We thoroughly evaluate DeepSpectrum using various test data at different signal-to-interference ratio (SIR) levels. Our evaluation results show that at $0\mathrm{~dB}$ SIR, the average identification accuracy for the four signals is over $\mathbf{9 9 \%}$ and the average error of central frequency is only 21KHz, which indicates that we can get the channel of Wi-Fi, Zigbee, and Bluetooth correctly. As for Lora with flexible central frequencies, the average bandwidth error is only 76KHz.

Leveraging emerging mobile edge computing and 5G networks, researchers proposed to offload the 3D rendering of interactive applications (e.g. virtual reality and cloud gaming) onto GPU-based edge servers to reduce the user experienced latency. A task offloading problem arises, that is where to offload rendering tasks such that each user will experience tolerable delay and meanwhile the cost of used servers is minimized. The multi-dimensional resource sharing feature of rendering tasks makes the problem challenging. We formulate the task offloading problem into a boolean linear programming. We propose a sharing-aware offloading algorithm which decomposes the problem into two subproblems (user assignment and server packing) and solves them alternately and iteratively. We compare our algorithm with the one without resource sharing in consideration, and the simulations demonstrate that our method can effectively reduce cost as well as satisfy delay requirement.

With the rapid growth of Internet of Things, the number of heterogeneous wireless devices working in the same frequency band increases dramatically, leading to severe cross-technology interference. To enable coexistence, researchers have proposed a large number of mechanisms to manage interference. However, existing mechanisms have severe modifications in either the physical or MAC (medium access control) layers, making them hard to be deployed on commercial devices. In this paper, we design and implement SledZig to boost cross-technology coexistence for low-power devices through both enabling more transmission opportunities and avoiding interference. SledZig is fully compatible with the standard in both physical and MAC layers. It decreases the WiFi signal power on the channel of low-power devices while keeps the WiFi transmission power unchanged, through making constellation points in the overlapped subcarriers have the lowest power, which can be achieved by just encoding the WiFi payload. We implement SledZig on hardware testbed and evaluate its performance under different settings. Experiment results show that SledZig can effectively increase ZigBee transmissions and improve its performance over a WiFi channel under various WiFi data traffic, with as low as 6.94% WiFi throughput loss.

The coexistence of heterogeneous devices in wireless networks brings a new topic on cross-technology communication (CTC) to improve the coexistence efficiency and boost collaboration among these devices. Current advances on CTC mainly fall into two categories,physical-layer CTCandpacket-level energy modulation(PLEM). Thephysical-layer CTCachieves a high CTC data rate, but with channel incompatible to commercial devices, making it hard to be deployed in current wireless networks. PLEM is channel and physical layer compatible, but with two main drawbacks of the low CTC data rate and MAC incompatibility, which will induce severe interference to the other devices’ normal data transmissions. In this paper, we propose symbol-level energy modulation (SLEM), the first CTC method that is fully compatible with current devices in both channel and the physical/MAC layer processes, having the ability to be deployed in commercial wireless networks smoothly. SLEM inserts extra bits to WiFi data bits to generate the transmitting bits, so as to adjust the energy levels of WiFi symbols to deliver CTC information. We make theoretical analysis to figure out the performance of both CTC and WiFi transmissions. We also conduct experiments to demonstrate the feasibility of SLEM and its performance under different network situations.

Leveraging emerging edge computing and 5G networks, researchers proposed to offload the 3D rendering of interactive multimedia applications (e.g., virtual reality and cloud gaming) onto edge servers. For high resource utilization, multiple rendering tasks run in the same GPU server and compete against each other for the computation resource. Each task has its requirement for performance, i.e., QoS target. A significant problem is how to schedule tasks so that each preset QoS is met and the performance of all tasks are maximized. We make the following contributions. First, we formulate the problem into a QoS constrained max-min utility problem. Second, we find that using the common natural logarithm as a utility function overly promotes one performance but demotes another. To avoid this phenomenon, we design a special utility function. Third, we propose an efficient scheduling algorithm, consisting of a resolution adjustment algorithm and a frame rate fair scheduling algorithm, both of which interact with each other. The former selects resolutions for tasks and the latter decides which task to process. We evaluate our method with actual rendering data, and the simulations demonstrate that our method can effectively improve task performance as well as satisfy QoS simultaneously.

With the prosperity of Internet of Things, a large number of heterogeneous wireless devices share the same unlicensed spectrum, leading to severe cross-technology interference (CTI). Especially, the transmission power asymmetry of heterogeneous devices will further deteriorate this problem, making the low-power devices prohibited from data transmission and starved. This paper proposes an enhanced CCA (E-CCA) mechanism to mitigate CTI, so as to improve the performance and fairness among heterogeneous networks. E-CCA contains a signal identification design based on deep learning to identify the signal type within a tolerable time duration, it also contains a CCA adaptive mechanism based on the signal type to avoid CTI. As a result, the ZigBee devices could compete for the channel with WiFi devices more fairly, and the network performance can be improved accordingly. We set up a testbed based on TelosB, a commercial ZigBee platform, and USRP N210, which can be used as the WiFi platform. With the collected signals through USRP N210, over 99.9% signal identification accuracy can be achieved even when the signal duration is tens of microseconds. Simulation results based on NS-3 shows that E-CCA can increase the ZigBee performance dramatically with little throughput degradation for WiFi.

The coexistence of heterogeneous devices in wireless networks brings a new topic on cross-technology communication (CTC) to improve the coexistence efficiency and boost collaboration among these devices. Current advances on CTC mainly fall into two categories, physical-layer CTC and packet-level energy modulation (PLEM). The physical-layer CTC achieves a high CTC data rate, but with channel incompatible to commercial devices, making it hard to be deployed in current wireless networks. PLEM is channel and physical layer compatible, but with two main drawbacks of the low CTC data rate and MAC incompatibility, which will induce severe interference to the other devices’ normal data transmissions. In this paper, we propose symbol-level energy modulation (SLEM), the first CTC method that is fully compatible with current devices in both channel and the physical/MAC layer processes, having the ability to be deployed in commercial wireless networks smoothly. SLEM attaches the CTC bits to one WiFi data packet through adjusting the energy levels of WiFi symbols, achieving concurrent CTC and WiFi transmissions. We make theoretical analysis to figure out the performance tradeoff between CTC and WiFi, and also conduct experiments to demonstrate the feasibility of SLEM and its performance under different network situations.

We are on the threshold of the explosive growth in the global Internet-of-Things (IoT) market. Comparing with the legacy human-centric applications, machine type communication scenarios exhibit totally different characteristics, such as low throughput, delay insensitivity, occasional transmission, and deep coverage. Meanwhile, it also requires the terminal devices to be cheap enough and sustain long battery life. These demands hasten the prosperity of low power wide area (LPWA) technologies. Narrowband IoT (NB-IoT) is the newest Long Term Evolution (LTE) specification ratified by the third generation partner project as one of the LPWA solutions to achieve the objectives of super coverage, low power, low cost, and massive connection. Working in the licensed frequency band, it is designed to reuse and coexist with the existing LTE cellular networks, which endows it with outstanding advantages in decreasing network installation cost and minimizing product time-to-market. In this backdrop, it has been extensively regarded as one of the most promising technologies toward the IoT landscape. However, as a new LTE standard, there are still a lot of challenges that need to overcome. This paper surveys its evolutions, technologies, and issues, spanning from performance analysis, design optimization, combination with other leading technologies, to implementation and application. The goal is to deliver a holistic understanding for the emerging wireless communication system, in helping to spur further research in accelerating the broad use of NB-IoT.

The introduction of wirelessly charged electric buses (WCEBs) into current public transportation system attracts many attentions in recent years. As the wireless charging technology enables energy transfer from power transmitters to electric vehicles (EVs) on road, it provides a promising solution to reduce the huge cost of battery with large size and long charging time, which are two critical impediments for EV applications. However, the system cost of WCEBs is huge. Under the dynamic electricity demands and the fluctuating electricity prices, the system operating electricity cost highly depends on the charging schedule. In this paper, according to the typical day-ahead electricity market, we explore an optimal charging scheduling scheme in a WCEB system to minimize the system operating electricity cost, while the characteristic of WCEBs is considered. The price of electricity fluctuates with the accumulated energy demands in both spatial and temporal domains. We first present a day-ahead reserved wholesale electricity determination algorithm, in which, the average speeds of WCEBs are presumed. Then, we propose an optimal charging scheduling algorithm, in which the WCEB charging schedules in slots are optimized sequentially. Both the reserved electricity and the predicted speeds in the slot are used. Simulation results demonstrate the efficiency of our proposed WCEB charging schedules.

The wireless local area networks (WLANs) have been widely deployed with the rapid development of mobile devices and have further been brought into new applications with infrastructure mobility due to the growth of unmanned aerial vehicles (UAVs). However, the WLANs still face persistent challenge on increasing the network throughput to meet the customer’s requirement and fight against the node mobility. Interference is a well‐known issue that would degrade the network performance due to the broadcast characteristics of the wireless signals. Moreover, with infrastructure mobility, the interference becomes the key obstacle in pursuing the channel capacity. Legacy interference management mechanism through the channel access control in the MAC layer design of the 802.11 standard has some well‐known drawbacks, such as exposed and hidden terminal problems, inefficient rate adaptation, and retransmission schemes, making the efficient interference management an everlasting research topic over the years. Recently, interference management through exploiting physical layer mechanisms has attracted much research interest and has been proven to be a promising way to improve the network throughput, especially under the infrastructure mobility scenarios which provides more indicators for node dynamics. In this paper, we introduce a series of representative physical layer techniques and analyze how they are exploited for interference management to improve the network performance. We also provide some discussions about the research challenges and give potential future research topics in this area.

This paper represents the design, feasibility evaluation and performance validation of ICMR, a novel cross layer protocol that can maximize concurrent transmissions and avoid data frame interference in wireless networks, achieving higher throughput comparing with the 802.11 standard and other state-of-the-art protocols. Observations on the 802.11 standard reveal that nodes degrade the network throughput from two aspects, including the so-called CF-CA problem and varied-IR problem, and these problems will make nodes around both the transmitter and receiver of the ongoing link waste concurrent transmission opportunities. A state-of-the-art protocol IRMA is proposed to improve the network throughput through solving the two problems at the transmitter side. In this paper, a new ICMR protocol is proposed to solve both problems at the receiver side to further improve the network throughput through discernible interference cancellation, a physical layer mechanism that can successfully detect data frames when collided by control frames. Hardware experiments based on USRP2 demonstrate the feasibility of the discernible interference cancellation mechanism, and simulations based on ns-2 confirm that ICMR outperforms the 802.11 standard and other protocols significantly.

Interference management through power control in wireless ad hoc networks has both the hidden terminal problem which induces collisions, and the exposed terminal problem which prohibits concurrent transmissions. Both problems are caused by the varied interference range induced by the adjusted transmission power. Through observing that the nodes adopt the power control mechanism induces collisions in one scenario and miss concurrent transmission opportunities in two scenarios, this paper presents IAPC (Interference-Aware Power Control), a novel protocol to improve the network throughput from these aspects. IAPC makes the interference range of each link covered by its CTS (Clear-To-Send) transmission through utilizing a signature detection process, so as to avoid interference. Meanwhile, it lets CTS convey the transmission power information of this link to make the neighboring node determine a limited transmission power, which tries to make the neighboring node outside the interference range of the ongoing link, thus can exploit concurrent transmissions. Simulation results based on ns-2 show that IAPC can outperform the other protocols significantly.

A perfect bidirectional communication network is a common assumption in smart grids. However, it is unrealistic, especially in the neighborhood area network of microgrids. Due to the channel fading, large volumes of transmission data, and considerable communication cost, the imperfect communications affect the system performance directly. In this paper, we consider the uncertainty of imperfect communications in both supply and demand sides, which affects the microgrid system performance in terms of the packet loss ratio of the power demand data transmission and the forecasting accuracy ratio of the renewable energy generation. We analyze the impacts of imperfect communications on the demand response management (DRM) performance under the real-time pricing scheme. An optimization problem is formulated first to maximize the DRM performance of the microgrid system. As these impacts can be mitigated by using sufficient spectrum resources, we then propose a spectrum resource allocation scheme that considers different characteristics of transmission data and system communication cost to balance the tradeoff between the DRM performance and the incurred communication cost. We introduce a joint optimization problem that not only maximizes the DRM performance but also minimizes the communication cost. Simulation results reveal the impacts of imperfect communications on the DRM performance and power price, and the efficiency of the proposed optimization problems.

Energy efficiency is a critical issue of wireless devices. As the packets are broadcast to the devices in the wireless transmission media, all active neighboring devices have to spend their energy receiving the packets though the packets are not addressed to them, which is called as the packet overhearing problem. The real-world traffic trace analysis reveals that the energy cost on the packet overhearing accounts for the majority of the devices' energy inefficiency in high traffic wireless local area networks (WLANs). In this paper, we propose a novel sample-address sample-duration (SASD) scheme to solve the energy inefficiency of the packet overhearing problem. By adding a new SASD header, which contains the critical information, in front of the data packet at the physical layer, the SASD enables the devices to discern the required information in the energy-saving downclocking mode. Consequently, the non-destination devices of the packet can switch to the sleeping mode to avoid the packet overhearing problem. We demonstrate the feasibility of the SASD through hardware experiments and evaluate its energy-saving performance through ns-2 simulations. The results show that the SASD can greatly outperform the existing approaches in the high traffic WLAN scenario.

A reliable and energy-efficient communication infrastructure plays an important role in the success of data collection, transmission and control in smart grid (SG) networks. In order to satisfy the coverage and cost minimization requirements, in this paper, we introduce cognitive radio (CR) into a residential SG network in which the communication load and the available spectrum resource change with the power load in different time periods. To leverage this feature, we propose a spectrum access strategy selection scheme. Moreover, based on the proposed selection scheme, we propose an energy-efficient gateway on-off switching optimization scheme to minimize the energy consumption, while considering the sensing operation and transmission power control of SG nodes. Numerical results reveal that the proposed access strategy selection scheme can increase the average channel capacity of SG nodes considerably, and the proposed gateway on-off switching scheme can balance the tradeoff between the energy consumption and coverage requirement efficiently.

This paper represents the design, feasibility evaluation and performance validation of concurrency-based coordination mechanism (CCM), a novel cross-layer protocol that can coordinate among nodes effectively to avoid data packet interference in wireless local area networks (WLANs), achieving higher throughput compared to 802.11 standard and other state-of-the-art protocols. The design of CCM contains OpenCCM which is based on the architecture of software defined network to schedule the transmissions in both the uplink and downlink directions centrally to maximize transmission concurrency. It also contains an interference-resistant mechanism in the physical layer that can make the control message transmitted with the data packet simultaneously to eliminate the coordination overhead. Experiment results with USRP2 demonstrate the feasibility of the interference-resistant mechanism, and the simulations by ns-2 show that CCM can outperform other protocols significantly.

Wireless networks are propelled to improve the network throughput effectively to face the challenge of sustaining the rapid growth of data traffic and the high density of wireless nodes. Exposed terminals are a main source in wireless networks that degrades the network throughput performance through excessively avoiding interferences and forbidding concurrencies. To combat the exposed terminal problem and exploit the concurrent transmissions in wireless networks, we present the design of Interference Resistant Multiple Access (IRMA) in this paper, which can achieve higher throughput compared to the 802.11 standard. By observing that nodes in current protocols waste transmission opportunities in two different scenarios, IRMA exploits the concurrency in two aspects. IRMA proposes a signature detection method in the physical layer to combat control frames' collisions, thus exploits the concurrency at the transmitter side. IRMA also designs a new NAV update scheme in the MAC layer to differentiate the interference ranges of different transmission links, thus exploits the concurrency of all non-interfering links. Experimental results based on USRP2 demonstrate the feasibility of the signature detection method, and simulations based on ns-2 show that IRMA outperforms the 802.11 standard and other protocols significantly.

Hidden terminals are typical interference sources that can significantly reduce the throughput of a wireless network if it adopts the CSMA/CA MAC protocol. The RTS/CTS mechanism is a well-known solution to this hidden terminal problem. However, it only works well under the assumption that all hidden terminals can decode the CTS packets correctly. In the real world, the CTS packets might not be correctly received all the time due to either the CTS packets are unable to be decoded at remote hidden terminals or the CTS packets are collided with other packets at the hidden terminals. Both of these drawbacks can make the standard RTS/CTS mechanism fail to silence all hidden terminals, and deteriorate the throughput of the wireless network. In this paper, we present the RTS/S-CTS mechanism, a novel symbol-level detection mechanism that combats these two drawbacks. The RTS/S-CTS frames make slight changes to the standard RTS/CTS frames, and can be compatible with the standard 802.11 MAC layer. We design the symbol-level detection decoder (SLDD) and NAV decision algorithm that enable the S-CTS frame to be correctly detected from collisions and by remote hidden terminals. We build a testbed of RTS/S-CTS with GNURadio/USRP2 software radio to demonstrate its feasibility and run ns-2 simulations to evaluate its performance. The results show that the RTS/S-CTS can achieve up to 63% throughput improvement in the random topology network scenario compared with the standard RTS/CTS.

This paper represents the design of Interference Resistant Multiple Access (IRMA), a novel cross layer protocol that can combat exposed terminals and exploit the concurrent transmissions in wireless networks, both helping in achieving higher throughput compared to the 802.11 standard and other recent protocols. By observing that nodes in current protocols waste transmission opportunities in two different scenarios, IRMA exploits the concurrency in two aspects. IRMA proposes a signature detection method in the physical layer to combat control frames' collisions, thus exploits the concurrency at the transmitter side. IRMA also designs a new NAV update scheme in the MAC layer to differentiate the interference ranges of different transmission links, thus exploits the concurrency of all non-interfering links. In addition, IRMA is based on the 802.11 standard so that it can be implemented into current devices. Experimental results based on USRP2 demonstrate the feasibility of the signature detection method, and simulations based on NS2 show that IRMA outperforms other protocols significantly.