Adjacent node

Definition

Adjacent refers to the relationship formed between the final node and the selected nearby router in order to exchange routing information. Adjacent nodes are nodes that share a section of transmission medium.

Transmitting data between two adjacent nodes (between a host and a router or between two routers) is direct transmission (point-to-point). At this time, you need to use a special link layer protocol. When transmitting data between two adjacent nodes, the data link layer assembles the IP datagrams handed over from the network layer into a frame, and transmits the frame "transparently" on the link between the two adjacent nodes Data. Each frame includes data and necessary control information, such as synchronization information, address information, error control, etc.

The main purpose of flow control between adjacent nodes is to make full use of the line frequency band and the node's buffer zone, and when it is implemented, it is integrated with the error recovery procedure. The basic technologies involved are: response, window technology, retransmission strategy, buffer management.

Wireless sensor network security protocol based on the cooperation of neighboring nodes

Wireless sensor network is a distributed network composed of base stations and a large number of sensor nodes, which may be deployed in the In an environment where people are on duty, it is extremely vulnerable to physical damage and man-made attacks. Compared with traditional networks, sensor network nodes have a simpler structure and are limited in storage space, computing power, bandwidth, and communication energy. Therefore, designing an energy-efficient wireless sensor network security protocol becomes a challenge.

An improved wireless sensor network security protocol based on the cooperation of neighboring nodes is now proposed. While improving the survivability of the system, it further reduces the energy consumption in the key distribution process, so it is more suitable for engineering Application.

Assuming that a sensor network is composed of one or more base stations, the base stations are safe and reliable, and are connected to the external network. The sensor nodes have established a tree-like routing structure, and the nodes are deployed randomly. These nodes will be deployed in an area where the location is unknown and unattended. The wireless communication cannot be secured, the channel is extremely susceptible to monitoring, and the information is susceptible to theft and tampering. , Or subject to replay attacks. In addition, these nodes are extremely easy to be captured. If the node is captured, the attacker can obtain all the information in the node.

We have designed a multi-key mechanism similar to LEAP and NEKAP protocols to meet the security needs of multiple forms of communication. Each node contains four kinds of keys:

Individual keys, used for communication between nodes and base stations;

Pair keys, used for communication between nodes Point-to-point communication;

Cluster key, used for communication with neighboring nodes;

Global key, used for communication with base station.

The key distribution protocol will be described below (take node A as an example).

(1) Initialization stage:

Step 1: Preset a unique node ID in each node;

Step 2: Create for each node Master key () and authentication master key (), these two keys are only known by the base station;

Step 3: Calculate the authentication key () for each node and install it in the node, ;

Step 4: Preset individual key (), cluster key () and global key () in each node.

(2) Broadcast stage:

Step 1: After the broadcast stage starts, each node sends the following broadcast information to neighboring nodes in broadcast mode:

Step 2: After a period of waiting, the broadcast information transmission of all nodes ends.

Step 3: The base station announces the authentication master key (), to all nodes. At this time, the neighboring node can calculate the authentication key () of node A through the authentication master key (), and then parse out the cluster key () of node A in the data packet and the first one in the one-way key chain. A key and build your own list of neighboring nodes. At the same time, it can also verify whether the source of the data packet is legitimate, and finally, remove the authentication key () and the authentication master key ().

Step 4: When the node completes the above steps, the node starts to broadcast its neighbor node list to neighboring nodes,

When the node and each other receive each other’s neighbor node list After that, the pair key () for communication between AB can be established. The pair key between nodes is calculated from their cluster keys and their nodes.

(3) Malicious node detection and diagnosis stage:

Replay attack is a malicious node copying a received message and sending it to other nodes through replay. An attack method that pretends to be a legitimate node. When other nodes receive the message, they will calculate the wrong communication distance and use the wrong signal strength. Traditional networks often use timestamps or serial numbers to resist replay attacks.

The method of using timestamp must be based on a strict time synchronization mechanism, while the method of serial number is even more unsuitable for sensor networks. This scheme uses an acknowledgement, that is, a method to confirm characters. Solve the above problems.

Step 1: When the node receives the node’s information, it will generate one and send it to, encrypt the key () to prevent it from being forged, and then it will be stored in the temporary cache until Return.

Step 2: After the node receives it, it resets its timer, and then verifies it. After the verification is passed, the node adds a timestamp to it, and then returns it to the node. If it is received within a reasonable time, the node is a trusted node, otherwise the node is an untrusted node. Because this information may be forwarded by malicious nodes, resulting in too long transmission delay, which may not be received within a reasonable time. Then the node can delete the node from the adjacent node list.

It also includes a highly efficient local broadcast authentication scheme based on a one-way key chain. In order to prevent replay attacks, the scheme provides a malicious node detection stage to monitor and remove malicious nodes. The solution can be used to verify various link layer information, and by discarding the information that has not passed the verification, the access control of the node is realized, thereby avoiding various attacks from external nodes.

An improved ant colony algorithm based on the characteristics of adjacent nodes

Ant colony algorithm is a heuristic search algorithm that simulates the foraging behavior of ants in nature. Proposed by scholars such as Italian D. Subsequent researchers from various countries also proposed a variety of excellent ant colony optimization algorithms to solve combinatorial optimization problems. Starting from the application of ant colony algorithm in traveling salesman problem (Travelling　Salesman　Problem, TSP), an improved ant colony algorithm based on path characteristics is proposed. The algorithm mutates the path according to the relationship between neighboring cities and expands the population diversity of the ant colony, so that a shorter path can be found before the algorithm stagnates, and the optimal path solution can be continuously approached. At the same time, the adaptive adjustment mechanism of pheromone volatilization factor and the idea of ​​public path are introduced to adjust the convergence speed to improve the global search ability.

In the ant colony algorithm, the rule for the first ant to choose from the city to the next city is:

At that time:

At that time, the ants choose the next city according to the transition probability formula (2).

Among them, is a constant between (0,1), is a random number uniformly distributed between (0,1), represents the pheromone concentration on the path between the city and the city, and is two The distance between cities. The heuristic factor reflects the relative importance of the amount of information accumulated by the ants in the movement process in guiding the ant colony search; the heuristic factor reflects the relative importance of the heuristic information in guiding the ant colony search during the movement of the ant; Ant's current set of feasible cities. The pseudo-random rule composed of equations (1) and (2) tends to choose a short path with greater pheromone concentration. After each search, the pheromone of each path is updated according to formulas (4)～(6):

Among them, represents the total number of ants, represents the information volatilization rate, and represents the ants in the current cycle The amount of information left on the path in the current cycle represents the increase in the amount of information left on the path by all ants that have gone through the path in this cycle, represents the total amount of information released by the ants in one cycle, and represents the first ant in the current cycle The length of the path taken in. This kind of information update rule can enhance the positive feedback performance of the algorithm information and improve the convergence speed of the system search.

The improved algorithm steps can be summarized as follows:

Step 1 initialization parameters: number of cities, number of ants, ant colony algorithm iteration counter, ant colony algorithm allowable maximum number of iterations, ant colony algorithm Group pheromone and so on.

Step 2 select some of the ant colonies and the first group according to formulas (1) and (2) to select cities, generate path solutions, and mutate each path.

Step 3 records the optimal and sub-optimal solutions in the solution set of Step 2 and calculates their common path.

Step 4 takes the remaining ant colony from step 2, and uses the public path of step 2 as the necessary road section. The city on the non-public path performs state transition according to formulas (1) and (2). Connect into a loop, generate path solutions, and mutate each path.

Step 5 solves the optimal solution of the current iteration. If it is less than the current optimal solution, update the optimal and sub-optimal solutions and go to step 7; otherwise, go to step 6.

Step 6 When the optimal solution of the current iteration is less than the current sub-optimal solution, update the sub-optimal solution. Steps follow formulas (4) ~ (6) to update the pheromone.

Step 8 The ant colony algorithm iteration counter is incremented by 1, if the maximum number of iterations is reached, go to step 9, otherwise go to step 2.

Step 9 outputs the optimal path and its length.