A sleep-wakeup scheduling method for linear wireless sensor networks

Abstract

The application provides a sleep-wake scheduling method for linear wireless sensor networks, and relates to the field of wireless sensors. The method first flexibly adjusts the duty cycle of the node by analyzing the initial position of the node, the distance relative to the base station and the current residual energy when the linear wireless sensor network is running; determines the depth of the node according to the initial position of the node, and embeds the energy information of the node into the data packet for exchange when the linear wireless sensor network is running, so as to realize real-time global planning and sleep scheduling of the corresponding depth. The sleep-wake scheduling method for linear wireless sensor networks provided by the application adopts a novel scheduling method based on node energy and depth, instead of the traditional MAC layer protocol, and through the introduction of the consideration of energy and depth, the duty cycle of the global node can be more accurately and flexibly controlled.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wireless sensor, and particularly relates to a sleep-wakeup scheduling method for linear wireless sensor network. BACKGROUND

[0002] In the current field of wireless sensor application, many sensors are deployed in detection areas with linear characteristics, such as railway track, tunnel detection and mine excavation. The sensor nodes in these linear areas often face problems of unbalanced energy consumption and inefficient data transmission due to the longitudinal extension when deployed. The traditional wireless sensor network routing protocol is not designed for the characteristics of linear areas, so the problems of node energy consumption balance, data transmission quality guarantee and network life cycle extension have not been effectively solved in application areas with long communication distance.

[0003] In linear sensor networks, data packets are exchanged and forwarded between nodes through single-hop, multi-hop and grouped multi-hop. Due to the linear distribution of nodes, nodes close to the base station cannot share high load tasks through multiple nodes as in mesh topology, resulting in high energy consumption. On the contrary, nodes far from the base station have a large amount of energy left due to low load. At present, domestic and foreign researches mainly aim at traditional topological structure, aiming to ensure the efficiency of data transmission and prolong the network life cycle, focusing on reducing power consumption and balancing load, usually through routing methods and clustering algorithm to solve these application requirements.

[0004] The existing methods can indeed play their role in some common application fields, achieving the purpose of energy saving and prolonging the network life cycle. However, these methods are not designed for linear sensor networks. Due to the longitudinal length of linear areas, which cannot be ignored, the communication distance and routing difficulty are increased. The traditional routing algorithm cannot fully exert its maximum advantage when applied to linear sensor networks.

[0005] In addition, the accompanying energy consumption caused by node state transition is often ignored in existing researches. Blindly designing energy saving from the perspective of routing and clustering has certain limitations. In actual application, flexible control of node sleep-wakeup time and data sending-receiving duration can more effectively solve the problem of energy consumption balance. Therefore, the applicability of existing solutions in linear sensor networks is limited, and it is difficult to meet the requirements of energy consumption management and efficient data transmission of this type of network. SUMMARY

[0006] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art, and to provide a sleep-wakeup scheduling method for linear wireless sensor network, which solves the technical problem that in linear wireless sensor network, some nodes with excessive load die first due to distance, causing the routing link to fail prematurely.

[0007] To solve the above technical problems, the technical scheme adopted by the present application is: a sleep wake-up scheduling method for linear wireless sensor network, comprising the following steps:

[0008] Step 1: initialization stage of linear wireless sensor network, each node transmits an initial data for recording hop count according to its initial position and transmits to base station, the initial data is the sensing data generated by the node, then the base station obtains the initial information of the related node, the initial information of the node includes node ID, hop count, source address, the hop count is the depth, the calculation steps are as follows:

[0009] Step 1.1: model the topology structure of linear wireless sensor network as a directed graph G:

[0010] G=(V,E)

[0011] Wherein, G represents a directed graph, V represents a node set, E represents an edge set; there is a directed edge (v i ,v i+1 ) between nodes v i and v i+1 ;

[0012] Step 1.2: each node calculates the local depth value according to the distance with adjacent nodes; the calculation formula of the depth d i of node v i is as follows:

[0013] d i =min(d i -1+1,d i+1 +1)

[0014] Wherein, d i-1 and d i+1 represent the depth values of nodes v i-1 and v i+1 respectively, the depth value of v i takes the minimum value of the depth values of adjacent nodes plus 1;

[0015] Step 1.3: since the local depth values calculated by each node are inconsistent, it is necessary to adjust the depth values between nodes through global information transmission, therefore, after the local depth value calculation is completed, the global depth value adjustment is carried out for the whole linear wireless sensor network, including the following steps:

[0016] Step 1.3.1: exchange and compare the depth value of each node itself with the depth value of adjacent nodes, and update to the minimum value between them at the same time;

[0017] Step 1.3.2: Through multiple iterations of passing, ensure that each node in the linear wireless sensor network can obtain a depth value corresponding to the shortest path length to the base station node;

[0018] Step 1.4: Upload the depth value of each node relative to the base station node to the base station as the initialization information of the node;

[0019] Step 2: During the operation of the linear wireless sensor network, each node obtains its own residual energy in real time, and uploads the residual energy of each node to the base station; the node detects its own residual energy and energy consumption rate in real time when transmitting or forwarding sensing data, and embeds the detected information into the data packet;

[0020] Set node v i The energy consumption rate at time t is R i (t), the energy consumed by the node within the time interval Δt is ΔE i = R i (t) × Δt, the residual energy E i of node v i at time t can be represented as the initial energy minus the cumulative energy consumption from the initial time to the current time, and the formula is:

[0021]

[0022] Where E i0 is the initial energy of node v i , and τ represents the integral variable;

[0023] Step 3: After the base station collects the depth value and residual energy value of the node, it calculates the comprehensive parameter of each node at a certain depth value; design an adaptive algorithm to consider depth and energy comprehensively to balance the energy consumption of the node, and dynamically adjust the weight parameter in the comprehensive parameter according to the working state of the node, including the following steps:

[0024] Step 3.1: For the depth value d i , adopt a linear function for normalization processing, and the formula is as follows:

[0025]

[0026] Where Depth_Norm i is a parameter related to depth information in the comprehensive parameter, and max(d) and min(d) represent the maximum and minimum values of all node depth values, respectively;

[0027] Step 3.2: For the energy value E iBecause the energy consumption is usually exponential, the logarithmic function is used for normalization, and the formula is as follows:

[0028]

[0029] wherein, is a parameter related to energy information in the comprehensive parameter, represents the initial energy of the node i, and min(E) represents the minimum value of the residual energy of all nodes;

[0030] Step 3.3: Integrate the node depth and energy to define a comprehensive parameter P i The depth parameter and the energy parameter are weighted and averaged to represent the depth and energy comprehensive index of the node i, and the formula is as follows:

[0031] P i =α×Depth_Norm i +(1-α)×Energy_Norm i

[0032] wherein, a is a depth weight parameter, used to balance the relative importance of depth and energy;

[0033] Step 4: The base station formulates the sleep-wake duration ratio of nodes with different depths according to the calculated comprehensive parameter; the nodes are divided into several energy consumption stages according to the comprehensive parameter, and the sleep-wake duration ratio of each energy consumption stage is allocated;

[0034] Step 5: The node selects the transmission mode of single-hop, multi-hop or grouped multi-hop according to the scheduling signaling issued by the base station;

[0035] Step 6: During the operation of the linear wireless sensor network, the base station monitors the state of the linear wireless sensor network in real time, and dynamically adjusts the sleep-wake duration ratio and the transmission mode selection method according to the depth and energy condition of the node.

[0036] The beneficial effects produced by the above technical scheme are that the sleep-wake scheduling method for the linear wireless sensor network provided by the application has the following advantages by tracking and analyzing the energy of the nodes in the linear wireless sensor network in real time and combining the depth of each node in the linear wireless sensor network for unified calculation and planning:

[0037] 1. Energy consumption balance:

[0038] 1.1: Technical means: The application tracks and analyzes the energy state of each node in real time, and plans in combination with the depth of the node in the linear wireless sensor network, and according to the priority, divides the best sleep-wake time ratio of the nodes with different depths in the operation of the linear wireless sensor network;

[0039] 1.2: Advantage: By flexibly adjusting the ratio of the effective signal time of the node to the total cycle, i.e., the duty cycle, the sleep duration of each node is dynamically adjusted according to the current residual energy and the depth determined according to the initial position when the linear wireless sensor network is running, thereby maximizing energy saving, so that the global node can achieve the maximum balance of energy efficiency while ensuring the transmission of detected sensor data on the energy consumption level.

[0040] 2. Sensing data transmission efficiency:

[0041] 2.1: Technical means: At the routing algorithm level, by considering the energy state and depth of the node, the duty cycle of the node is flexibly controlled to solve the problem of low efficiency of transmission of detected sensor data caused by fixed sleep and wake-up scheduling.

[0042] 2.2: Advantage: Compared with the method of simply adjusting the sleep and wake-up time at the MAC layer in the prior art, the present application more accurately controls the duty cycle of the global node by introducing the comprehensive consideration of energy and depth, thereby improving the efficiency of transmission of sensing data.

[0043] 3. Prolong the network life cycle:

[0044] 3.1: Technical means: A dynamic adjustment mechanism combining node energy and depth is adopted to adjust the working state of each node in real time, avoiding the premature depletion of energy of some nodes due to high load, and also preventing other nodes from wasting energy due to low load.

[0045] 3.2: Advantage: By optimizing the energy use of each node, the entire linear wireless sensor network is balanced in terms of energy consumption, thereby significantly prolonging the overall life cycle of the linear wireless sensor network. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A flow chart of a sleep and wake-up scheduling method for a linear wireless sensor network is provided in the embodiments of the present application.

[0047] Figure 2 The single-hop, multi-hop, and grouped multi-hop routing transmission mode schematic diagram provided by the embodiments of the present application is shown in the figure, wherein (a) is a single-hop routing, (b) is a multi-hop routing, and (c) is a grouped multi-hop routing. DETAILED DESCRIPTION

[0048] The specific embodiments of the present application will be further described in detail below in combination with the drawings and examples. The following examples are used to illustrate the present application, but are not used to limit the scope of the present application.

[0049] In this embodiment, a sleep and wake-up scheduling method for a linear wireless sensor network, as shown in Figure 1 the figure, includes the following steps:

[0050] Step 1: Linear wireless sensor network initialization phase, each node according to its initial position sends an initial data used to record the number of hops and is transmitted to the base station, the initial data is the sensor data generated by the node, then the base station obtains the initial information of the related node, the initial information of the node includes node ID, hop count, source address, the hop count is the depth, the calculation steps are as follows:

[0051] Step 1.1: Model the topology structure of the linear wireless sensor network as a directed graph G:

[0052] G = (V, E)

[0053] Wherein, G represents a directed graph, V represents a node set, E represents an edge set; there is a directed edge (v i , v i+1 ) between nodes v i and v i+1 ;

[0054] This modeling method of the graph can accurately reflect the physical structure of the linear wireless sensor network and the connection relationship between the nodes;

[0055] Step 1.2: Each node calculates the local depth value according to the distance with the adjacent node; the calculation formula of the depth d i of the node v i is as follows:

[0056] d i = min(d i-1 +1, d i+1 +1)

[0057] Wherein, d i-1 and d i+1 represent the depth values of nodes v i-1 and v i+1 respectively, the depth value of v i takes the minimum value of the depth values of the adjacent nodes plus 1;

[0058] Step 1.3: Since the local depth values calculated by each node are inconsistent, it is necessary to adjust the depth values between the nodes through global information transmission, therefore, after the local depth value calculation is completed, the global depth value adjustment is carried out for the entire linear wireless sensor network, including the following steps:

[0059] Step 1.3.1: Exchange and compare the depth value of each node itself with the depth value of the adjacent node, and update to the minimum value between the two at the same time;

[0060] Step 1.3.2: Through multiple iterations of passing, ensure that each node in the linear wireless sensor network can obtain a depth value corresponding to the shortest path length to the base station node;

[0061] Step 1.4: Upload the depth value of each node relative to the base station node to the base station as the initialization information of the node;

[0062] The algorithm pseudocode in this embodiment is as follows:

[0063]

[0064]

[0065] Step 2: During the operation of the linear wireless sensor network, each node obtains its own residual energy in real time, and uploads the residual energy of each node to the base station; the node detects its own residual energy and energy consumption rate in real time when transmitting or forwarding sensing data, and embeds the detected information into the data packet;

[0066] Let node v i The energy consumption rate at time t is R i (t), the energy consumed by the node within the time interval Δt is ΔE i = R i (t) × Δt, the residual energy E i of node v i at time t can be represented as the initial energy minus the cumulative energy consumption from the initial time to the current time, and the formula is:

[0067]

[0068] Where E i0 is the initial energy of node v i , and τ represents the integral variable;

[0069] In order to upload the dynamically updated self-energy consumption rate information to the base station, the node can periodically send the current self-energy consumption rate to the base station; in this way, the base station can know the current self-energy consumption rate of each node in real time, so as to more accurately evaluate the self-energy consumption state of the node;

[0070] Step 3: After the base station collects the depth value and residual energy value of the node, it calculates the comprehensive parameter of each node at a certain depth value; design an adaptive algorithm to consider the depth and energy comprehensively to balance the energy consumption of the node, and dynamically adjust the weight parameters in the comprehensive parameter according to the working state of the node, including the following steps:

[0071] Step 3.1: For the depth value d i, the linear function is used for normalization, and the formula is as follows:

[0072]

[0073] wherein, Depth_Norm i is a parameter related to depth information in the comprehensive parameter, max(d) and min(d) represent the maximum and minimum values of all node depth values respectively;

[0074] Step 3.2: for the energy value E i Because the energy consumption is usually exponential, the logarithmic function is used for normalization, and the formula is as follows:

[0075]

[0076] wherein, is a parameter related to energy information in the comprehensive parameter, represents the initial energy of node i, and min(E) represents the minimum value of all node residual energies;

[0077] Step 3.3: integrate the node depth and energy to define a comprehensive parameter P i The depth parameter and the energy parameter are weighted and averaged to represent the depth and energy comprehensive index of node i, and the formula is as follows:

[0078] P i =α×Depth_Norm i +(1-α)×Energy_Norm i

[0079] wherein, α is a depth weight parameter, used to balance the relative importance of depth and energy;

[0080] Step 4: the base station formulates the sleep-wake duration ratio of nodes with different depths according to the calculated comprehensive parameter; for nodes with depth values greater than the average value or residual energies greater than the energy setting threshold, more wake-up time is planned; for nodes with depth values less than the average value or residual energies less than the energy setting threshold, more sleep time is planned; the nodes are divided into several energy consumption stages according to the comprehensive parameter, and the corresponding sleep-wake duration ratio is allocated to each energy consumption stage;

[0081] In this embodiment, multiple thresholds are set, the comprehensive parameter is divided into multiple levels, and then the corresponding sleep-wake duration ratio is formulated according to each level, and the pseudo code of the algorithm mechanism is as follows:

[0082] Multiple thresholds are set, the comprehensive parameter is divided into multiple levels, and then the corresponding sleep-wake duration ratio is formulated according to each level, and the pseudo code of the algorithm mechanism is as follows:

[0083]

[0084]

[0085] The adjustment of continuity is provided by interpolation calculation, so that the sleep wake-up duration ratio adjustment has transition; the sleep wake-up duration ratio is dynamically calculated, so that the adjustment is more flexible, the node demand under different energy consumption states can be better adapted, and the estimation can be more accurate, thereby improving the energy efficiency performance of the linear wireless sensor network;

[0086] Step 5: The node selects a transmission mode of single hop, multihop or packetized multihop according to the scheduling signaling issued by the base station; for a node that needs faster transmission and has sufficient energy, the single hop or packetized multihop mode is used to transmit the detected sensing data; for a node with low energy, the multihop mode is used to transmit the detected sensing data, and the adjacent nodes are assisted to share the load to reduce the energy consumption pressure;

[0087] The embodiment adopts flexible switching of different transmission modes to realize energy consumption balancing. The single hop, multihop and packetized multihop routing transmission modes are as shown in FIG. 5. Figure 2 It can be analyzed that the multihop routing can send data packets in the minimum communication range, but in the linear topology structure, the nodes closer to the base station mean to undertake more forwarding tasks, and high load brings high power consumption; the packetized multihop can adjust the transmission frequency packet transmission according to the application demand and energy consumption state, and then the next hop performs packet forwarding, which has greater flexibility and fault tolerance compared with the multihop routing; the single hop routing is the most traditional transmission mode, that is, the source node and the target node perform point-to-point transmission to maximize the data transmission quality, but the relatively high frequency of transmission data will cause a large amount of energy consumption, and there may be a case that the transmission distance exceeds the communication distance;

[0088] The embodiment introduces a time mechanism in the running phase of the linear wireless sensor network. In order to reduce the message collision between neighbor nodes, a variable V r is randomly introduced into the to-be-transmitted sensing data in the memory queue before the sensing data packet is sent, and the value of the variable V is a random real number between [0.7, 1];

[0089] Step 6: In the running process of the linear wireless sensor network, the base station monitors the state of the linear wireless sensor network in real time, and dynamically adjusts the sleep wake-up duration ratio and the transmission mode selection method according to the depth and energy condition of the node;

[0090] When the energy consumption of the node reaches a certain threshold or the linear wireless sensor network load changes, the transmission method is adjusted in time to maintain the balance and stability of the linear wireless sensor network.

[0091] The mechanism pseudo code for selecting the transmission method according to the comprehensive parameter is as follows:

[0092]

[0093] It can be analyzed that the packet multi-hop is a flexible transmission method, which can dynamically adjust the packet size according to the application requirement, and is an optimized selection instead of the single-hop transmission and the multi-hop transmission. For example, the node close to the base station should select the multi-hop to ensure the data transmission quality when the energy is sufficient, and select the packet multi-hop when the energy is low, so as to sleep for as long as possible, and relieve the high load by the neighbor node, and prolong the life cycle; the node far from the base station can select the single-hop or the packet multi-hop with large packet when the energy is sufficient, but can also select the packet multi-hop with small packet or the multi-hop when the energy is low.

[0094] The comprehensive parameter calculated by the energy parameter and the depth parameter is a dynamic reference value, and since the information of the load and the position is introduced at the same time, it has a strong reference function in the selection of the sleep-wake scheduling and the transmission data method.

[0095] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope defined by the claims of the present application.

Claims

1. A sleep-awake scheduling method for linear wireless sensor networks, characterized in that, The method comprises the following steps: Step 1: linear wireless sensor network initialization phase, each node transmits an initial data for recording hop count according to its initial position and transmits to the base station, the initial data is the sensing data generated by the node, and then the base station obtains the initial information of the related node, the initial information of the node includes node ID, hop count, source address, and the hop count is the depth; Step 2: during the operation of the linear wireless sensor network, each node acquires the residual energy of itself in real time, and uploads the residual energy of each node to the base station; the node detects the residual energy and the energy consumption rate in real time when transmitting or forwarding the sensing data, and embeds the detected information into the data packet; Step 3: after the base station collects the depth value and the residual energy value of the node, the comprehensive parameter of each node at a certain depth value is calculated; the weight parameter in the comprehensive parameter is dynamically adjusted according to the working state of the node; Step 3.1: For depth value d i , a linear function is used for normalization, as follows: where Depth Norm = max(d) - min(d) i is one of the parameters in the synthesis parameter related to depth information, and max(d) and min(d) represent the maximum and minimum of all node depth values, respectively. Step 3.2: For the energy value E i Because the energy consumption is usually exponential, the logarithmic function is used for normalization, and the formula is as follows: wherein is one of the parameters of the synthesis parameter relating to energy information, denotes the initial energy of node i, min (E) denotes the minimum of the remaining energies of all nodes; Step 3.3: Integrate the node depth and energy to define a comprehensive parameter P i The depth parameter and the energy parameter are weighted and averaged to represent the comprehensive index of the depth and energy of the node i, and the formula is as follows: P i = a x Depth Norm i + (1 - a) x Energy Norm i Wherein, α is the depth weight parameter, which is used to balance the relative importance of depth and energy; Step 4: the base station formulates the sleep-wake duration ratio of nodes at different depths according to the calculated comprehensive parameter; the nodes are divided into several energy consumption stages according to the comprehensive parameter, and the sleep-wake duration ratio of each energy consumption stage is allocated; Step 5: the node selects the transmission mode of single hop, multi-hop or grouped multi-hop according to the scheduling signaling issued by the base station; Step 6: during the operation of the linear wireless sensor network, the base station monitors the state of the linear wireless sensor network in real time, and dynamically adjusts the sleep-wake duration ratio and the transmission mode selection method according to the depth and energy condition of the node.

2. The sleep-wakeup scheduling method for linear wireless sensor networks according to claim 1, characterized in that: The step 1 comprises the following steps: Step 1.1: the topology structure of the linear wireless sensor network is modeled as a directed graph G: G=(V,E) Where G represents a directed graph, V represents the set of nodes, and E represents the set of edges; node v i and v i+1 There exists a directed edge (v) i ,v i+1 ); Step 1.2: Each node performs a local depth value calculation based on its distance to neighboring nodes; node v i The depth d i of v is calculated as follows: d i = min(d i-1 +1, d i+1 +1) Where, d i-1 and d i+1 They represent nodes v respectively i-1 and v i+1 The depth value, v i The depth value is the minimum of its depth value plus 1 of the depth values ​​of its adjacent nodes; Step 1.3: since the depth values calculated by each node locally are inconsistent, the depth values between nodes need to be coordinated through global information transmission, therefore, after the local depth value calculation is completed, the global depth value adjustment is performed on the entire linear wireless sensor network; Step 1.4: the depth value of each node relative to the base station node is uploaded to the base station as the initialization information of the node.

3. The sleep-wakeup scheduling method for linear wireless sensor networks according to claim 2, characterized in that: The step 1.3 comprises the following steps: Step 1.3.1: the depth value of each node itself is compared with the depth value of the adjacent node, and is updated to the minimum value between the two at the same time; Step 1.3.2: through multiple iteration transmissions, it is ensured that each node in the linear wireless sensor network can obtain the depth value corresponding to the shortest path length of the base station node.

4. The sleep-wakeup scheduling method for linear wireless sensor networks according to claim 3, characterized in that: The step 2 sets the node v i The energy consumption rate at time t is R i (t), the energy consumed by the node in the time interval Δt is ΔE i = R i (t) x Δt, the node v i The remaining energy E i (t) at time t can be represented as the initial energy minus the cumulative energy consumption from the initial time to the current time, the formula is: where is the initial energy of node v i , and τ represents the integration variable.

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